Express decision

ABSTRACT

A system includes a processor to: receive a description of a current decision that includes selecting an option; repeatedly display the description; for each option, receive an indication of selection of scale text specifying a degree of intensity or intensity for achieving or avoiding a possible outcome of the option, and derive an overall motivation based on the at least one selection; identify a best option based on the overall motivations; display the best option; compare the overall motivation of the best option to a threshold; in response to being less than the threshold, display a warning and a prompt for the operator to further consider the current decision; compare the overall motivation of the best option to the others; and in response to not exceeding all other overall motivations, by at least a threshold of difference, present a proximity warning and the prompt.

RELATED APPLICATIONS

This application claims the benefit of the priority date of U.S.Provisional Application 63/031,723 filed May 29, 2020 by AlexanderYemelyanov, and entitled EXPRESS DECISION, the disclosure of which isalso incorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to the field of employing anexperimentally developed understanding of the decision-making functionsof the human brain to provide apparatus and method of assisting thebrain of a decision maker in making a decision in which a choice must bemade from among multiple options. More particularly, the capacity of thefaster short term memory of the brain of the decision maker is augmentedto enable retention of more details concerning each of option. Also, foreach of those options, the decision maker is prompted through providinginput concerning those details in a manner that elicits and isindicative of both instrumental and value rationality. Such inputs arethen used to derives degrees of motivation for the multiple options, andto identify a best option from among the multiple options. Such degreesof motivation are also used to determine whether still moreconsideration of the decision is needed, including reconsideration ofdetails for each option and/or a re-framing of the decision. Suchreconsideration and/or re-framing may entail the addition of moreoptions and/or an explicit statement of goal(s).

2. Description of the Related Art

The question of how the human brain works, including how the human brainmakes decisions, has been a subject of study and speculation forcenturies. For much of that time, there have been few accepted conceptsof how the human brain works that have been truly based on objectiveobservation. Instead, many of such accepted concepts have tended to bemore reflective of cultural aspects of the societies in which theyoriginated. In more recent decades, advances in medical knowledge haveincreasingly shaped accepted concepts of how the human brain works,including encouraging an increased reliance on objective observation.However, societal factors still exert influence, such as recentsignificant advances in various technologies that have encouraged atendency to compare the human brain to various forms of machinery.Modernly, a better understanding of the many features of the human brainis seen as potentially leading to a wide variety of useful benefits in awide variety of areas, such as increasingly automating various tasks.

In recent decades, ever-accelerating advances in micro-processors andother micro-electronic technologies have lead to a temptation to viewthe human brain as being like a computer system, possibly with variousarchitectural augmentations to the classic Von Neumann computerarchitecture. This has lead to a corresponding temptation to view it aspossible to create an artificial equivalent to the human brain, or atleast portions thereof, using such technologies. By way of example, avariety of types of artificial neuron have been devised that combinememory storage components, a trigger function, and a set of inputs andoutputs that are at least vaguely inspired by the observed physicalstructures and electrical behavior of actual neurons. There has beensome success in combining such artificial neurons to form neuralnetworks intended to perform sensory input functions such as speechrecognition and the visual recognition of objects in a manner that isalso inspired by, and intended to mimic, particular portions of thehuman brain that are widely believed to be responsible for performingsuch functions (e.g., the Wernicke's area believed to be involved inspeech recognition, and the visual cortex believed to be involved in thevisual recognition of objects).

Such seeming vindication of such computer-like views of sensory-relatedaspects of the human brain have helped to reinforce a continuingcomputer-like view of the manner in which human decision makingfunctions, or at least the view that computer-related technologies couldbe used to mimic such functionality. Such views have tended to encouragethe adoption of various models of human decision making that tend toemploy mathematical modeling, and thus, are able to serve as analyticaltools with the aid of computing devices. An example of this is ExpectedUtility Theory (EUT), which has been employed in making and/orevaluating economic and/or business decisions. EUT attempts to determinewhich one of multiple options that are being considered in a decision isthe rational choice based on a quantifiable goal and a quantifiabledegree of risk for each option. Unfortunately, while EUT has beensuccessfully utilized for economic and/or business decisions, it hasproven far less successful in other areas of decision making. Thus, EUThas proven to be a relatively inaccurate model of human decision makingin areas outside of making economic and/or business decisions.

For over a century (well before the advent of current computer-relatedtechnologies), it has been argued that there are two basic types ofrationality employed by the human brain in making decisions. One ofthese types is “instrumental rationality” that focuses on whatever meansare most efficient/effective in achieving a goal. Thus, instrumentalrationality is often information-driven as it is often focused on thedetails that set forth pros and cons of the different available optionsfor how to reach a goal. The other of these two types is “valuerationality” that focuses on the desirability and/or moral/ethicalcorrectness of the goal. Thus, in contrast to instrumental rationality,value rationality is often driven by subjective belief and/or philosophyconcerning whether a goal is a good goal, or not. It has been positedthat human decision making may employ both types of rationality indiffering proportions of influence depending on the nature of a decisionbeing made and/or the conditions under which a decision is being made.

Thus, looking back at EUT, it may be argued that such decision makingmodels are destined to be unsuccessful in areas of decision makingoutside of economic and/or business decisions, because such decisionmaking models as EUT employ instrumental rationality almost to theexclusion of value rationality. Thus, there is no integration ofinstrumental and value rationality. This may be argued as logicallyfollowing from the fact that analyses of relativeefficiency/effectiveness in achieving a goal often lend themselves quitewell to computational techniques that minimize or maximize a quantity ofsomething (e.g., the computational techniques frequently used inoperations research), whereas analyses of the desirability and/ormoral/ethical correctness of a goal often do not.

While EUT may be representative of decision making models that may befaulted for excluding value rationality, the variety of decision makingthat is often seen day to day in the medical field may be faulted forexcluding instrumental rationality and/or for employing valuerationality based on values other than those of a patient. This hasproven to be the case especially in emergency situations in whichdecisions must be made quickly and/or where the patient is unconscioussuch that they cannot participate in decision making, at all. However,it has been found that this effectively remains the case even innon-emergency situations where the patient is fully able to participatein decision making concerning their own medical care, and wishes to doso.

In emergency situations in which decisions about providing healthcare toa patient must be made quickly and/or without the ability to consult thepatient (e.g., the patient is unconscious), doctors may be left withlittle more than their professional experience, intuition as to whatactions need to be taken, and/or various “fast-and-frugal” decisiontools that they may have been trained to use (e.g., the classic “ABC”airway, breathing & circulation first aid prioritization approach fortreating an unconscious patient). Thus, in emergency situations, doctorsfrequently follow well-trodden scripts of approaches to quickly identifywhat emergency medical problem is in front of them, and to quickly takewell-rehearsed steps to address the identified problem. Often, and outof necessity, this results in problem-centered care where there is animmediate goal of addressing the problem, and not much deliberationconcerning pros and cons of various options for doing so.

In effect, a decision making process based on a very rough form of valuerationality, and little or no instrumental rationality, is often theresult. Additionally, the rough form of value rationality that is usedis necessarily based on the values of the medical professional makingthe decisions and/or of whoever trained them. Fortunately, the variouswell-trodden scripts that are followed and the various well-rehearsedsteps to addressing the problems identified from following thosewell-trodden scripts do, quite often, bring about good results. However,mistakes are still made that might have been prevented with a morecomplete decision making process that integrates both instrumentalrationality and value rationality based on values of the patient.

Unfortunately, even in non-emergency situations in which there is moretime to make decisions, and in which the patient is available andactively seeking to participate in making decisions concerning their owncare, various factors associated with medical culture and thecomplexities of medical treatments often conspire to cause the decisionmaking to remain problem-centered, and to continue to fail toincorporate instrumental rationality, or value rationality based onvalues of the patient.

And yet, it has been observed that younger generations are not asroutinely trusting of doctors' decisions as older generations, and havebeen more willing to question the thinking employed by doctors, as wellas the results achieved. There is also an increased willingness inyounger generations to engage in litigation against medicalprofessionals in which the decisions of medical professionals arequestioned. Thus, doctors are increasingly encountering patients whoinsist on being more involved in planning their medical care, and whoare more likely to feel dissatisfied with the results (even arguablypositive results) if they feel somehow prevented from becoming soinvolved.

In response, various attempts have been made to provide various forms ofShared Decision Making (SDM) in the medical field in which varioustechniques have been devised for giving patients some amount of teachingconcerning their medical conditions and the available treatmentstherefor, so that they can become “empowered” to participate in makingdecisions concerning their own medical care. These efforts are oftenexpressed as being intended to provide more “patient-centered care”where a patient's input is taken into account so as to enable somedegree of “self-determination” for patients.

Unfortunately, many of such efforts to provide SDM have been generallyunsuccessful. The teachings that are provided to patients concerningtheir medical conditions and available treatments are often through theprovision of various “decision aids” that often include leaflets, audioand/or video recordings, and/or various forms of interactive media thatare intended to supplement whatever conversations may be had withmedical professionals. Unfortunately, despite being intended to betterenable patient participation in patient care decision making, such“decision aids” have often paradoxically had the effect of overwhelmingpatients with information to an extent that they are too lost in thedetails of possible options to be so enabled. A frequent result is thatpatients find themselves forced to revert to leaning on the judgment ofthe medical professionals that they wanted to share the decision makingprocess with such that the decision making process largely reverts backto being made by those medical professionals with little input from thepatients.

This unfortunate outcome also receives some degree of reinforcement froma medical culture in which many medical professionals are skeptical ofthe entire idea of patients becoming involved in making medicaldecisions. Such medical professionals view their patients as unqualifiedto make medical decisions, and such medical professionals feel a senseof vindication concerning this opinion from witnessing how overwhelmedpatients become under such circumstances. They observe how suchoverwhelmed patients ultimately put such decision making back in thehands of those medical professionals, which is where those medicalprofessionals believe it should have been all along. In a way, this alsoserves to encourage medical professionals to revert back to taking amore problem-centered approach to deciding what care to provide, since,in their view, the patient wasn't going to have much to say, anyway.

It may we be that some of the difficulty that patients encounter intrying to participate in decisions concerning their own health care mayresult from commonplace limitations of the human brain. Studies haveshown that the human brain generally has an upper limit of about four“chunks” of information that can be retained in short-term memory whereit is more readily available for being considered in making a decision.A “chunk” of information is a set of details concerning a topic that aparticular person has learned to associate with each other such thatthey are able to easily consider those details altogether. The processof learning such associations among such multiple details of aparticular topic is often referred to as “chunking” by those who studythe human brain. A simple example of “chunking” is the process oflearning a new (i.e., unfamiliar) phone number. Initially, each of theindividual digits are treated by the human brain as a separate chunk,which is part of the reason why retaining all of the digits of a newphone number in short term memory can be very challenging, at first.However, over time, with repeated use leading to increased familiarity,the human brain may begin to build associations among digits in a subsetof the phone number (e.g., the area code) leading to that subset ofdigits becoming as a single chunk. Eventually, with still more useleading to still more familiarity, the entire phone number begins to betreated as a single chunk, such that, eventually, multiple other chunksof information are able to be retained in short term memory along withthat phone number.

Thus, in the case of medical professionals, years of medical trainingenables the brain of a medical professional to build associations amongdetails associated with medical theories, conditions, diagnoses andtreatments. After such training, the brain of a medical professional isable to more easily treat relatively large sets of such detailsassociated with a particular medical condition as a single chunk thatfits comfortably within a large continuum of medical topics that havebecome a familiar background of topics committed to long-term memorywhere each such topic has a comfortably familiar label and/or shorthandbased on a comfortably familiar medical vocabulary that has also beencommitted to long-term memory. In contrast, the brain of a person whodoes not have such training has never formed such associations amongsuch details. Thus, each detail of the many details of that same singlemedical condition becomes a new and unfamiliar subject that is notassociated with anything else in either background knowledge or pastexperience that has been committed to long-term memory. This can causeeach such detail of a single medical condition to be treated as aseparate chunk for some time until, perhaps, enough medical knowledgehas been collected in long-term memory to enable the building of theassociations needed for “chunking” to be supported.

As a result, even considering the multiple details of just one optionfor what to do in response to just a single medical condition canswiftly overwhelm the limitations of short-term memory, thereby leadingto a patient being operationally overwhelmed in trying to participate indecisions about their own medical care. This result, and its associatedreinforcement of skepticism of patient participation in decision makingin medical culture, often conspire to prevent patients from fullyparticipating in medical decisions in a way that might have enabled themto bring their own instrumental rationality, as well as valuerationality based on their own values, into the decision making process.

Thus, both for such medical decisions as are described above, and fordecisions in other different situations, a need exists for a decisionmaking augmentation tool that better assists the brain of a decisionmaker in integrating both instrumental and value rationality in making adecision.

SUMMARY OF THE INVENTION

The present invention is a decision making augmentation system of one ormore devices that implements a method for both augmenting the short-termmemory of the brain of a decision maker, and guiding the decision makerthrough considering positive and negative aspects (“pros and cons”) ofeach option of a current decision in a manner that integrates bothinstrumental rationality and value rationality based on the values ofthe decision maker. Through prompts provided in a user interface, thedecision maker is guided through providing input such as the currentdecision to be made, a shorter and/or longer term goals that the currentdecision may be associated with, what options are being considered tochoose from in making the current decision, pros and cons of eachoption, degrees of intensity of approval/disapproval associated witheach option, and/or degrees of likelihood of occurrence/avoidanceassociated with each option. From at least the degrees of intensity andlikelihood provided as input by the decision maker for each option,degrees of motivation are derived for the multiple options, and are usedto identify a best option from among the multiple options. The bestoption may then be presented to the decision maker along with anindication of degree(s) of motivation associated with the best option.Based on the degrees of motivation derived for the multiple options,determinations may also be made as to whether the decision maker needsto further consider the current decision, and an indication to thateffect may also be presented to the decision maker.

The system may be operable in either a compact mode that is bettersuited for situations in which a current decision is to be made in arelatively short period of time, and an expanded mode for situations inwhich there is more time to more carefully consider a current decision.Upon commencing the use of the system, the decision maker may beprompted to choose from between these two modes based on time availablefor making the current decision.

In the compact mode, a decision maker may be prompted to enterdescriptive text that describes the current decision to be made, alongwith descriptive text that describes each option being considered in thecurrent decision. For each option, the decision maker may also beprompted to specify degrees of intensity and/or likelihood associatedwith each option. More specifically, the decision maker may be promptedto select scale text from among a menu of scale texts, where the scaletexts within each such menu define a range of degrees of positive ornegative intensity, or a range of degrees of positive or negativelikelihood, that is associated with that option.

In the expanded mode, a decision maker may be additionally prompted toenter descriptive text that describes a longer term goal and/or ashorter term goal that is related to the current decision, and that maybe affected by which option is selected in making the current decision.Additionally, for each option, the decision maker may be prompted toenter descriptive text that describes both a successful outcome and anunsuccessful outcome that may arise from that option. For eachsuccessful outcome of each option, and for each unsuccessful outcome ofeach option, the decision maker may be further prompted to select scaletext from among a menu of scale texts, where the scale texts within eachsuch menu define a range of degrees of positive or negative intensity,or a range of degrees of positive or negative likelihood, that isassociated with one of the possible outcomes.

Regardless of whether the compact mode is used or the expanded mode isused, the descriptive text entered by the decision maker to describe thecurrent decision, shorter and/or longer term goals, each of the options,and/or each possible successful and unsuccessful outcome of each optionmay not actually be used by the system as any form of input to theactual identification of a best option. Instead, such descriptive textmay be repeatedly and/or continuously visually presented back to thedecision maker in the user interface as a mechanism for aiding thedecision maker in keeping clearly in mind what the current decision is,what the shorter and/or longer term goals are, what each option is,and/or what each possible successful and/or unsuccessful outcome of eachoption is. In other words, the system assists the short-term memory ofthe decision maker in retaining such information, thereby freeing moreof the limited short-term memory of the decision maker for use inretaining other pieces of information pertinent to the current decision.Thus, such repeated presentation of such descriptive text to thedecision maker effectively serves to augment the short-term memory ofthe brain of the decision maker.

Such repeated presentation of such descriptive text to the decisionmaker also serves to reduce the expenditure of time and effort torepeatedly retrieve descriptions of the current decision, of the longerand/or shorter term goals, of each of the options, and/or of thepossible successful and/or unsuccessful outcomes of each option fromlong-term memory. From experimental observation, it is known that chunksof information stored within short-term memory of the human brain iseventually transferred to the long-term memory. As previously discussed,the retrieval of pieces of information from the long-term memory oftenentails the use of associations that are learned over time among piecesof information. It has also been observed through experimentation thataccessing pieces of information stored in long-term memory tends to takeconsiderably more time and effort than from short-term memory, possiblydue to the use of associations in making such accesses. The repeatedpresentation of such descriptive texts to the decision maker via theuser interface obviates the need to use such time and energy to makethose accesses.

Also regardless of whether the compact mode or the expanded mode isused, and in contrast to the descriptive texts, the scale textindications of degrees of intensity and likelihood selected by thedecision maker for each option, or for each of the successful andunsuccessful outcomes of each option, may be used by the system asinputs to the identification of a best option. More precisely, each ofthe selections of scale text by a decision maker may be correlated to acorresponding numeric scale value. For each option, those numeric scalevalues for intensity and likelihood may then be used to derive degreesof positive and negative motivation for that option. The degrees ofmotivation associated with each of the options may be used to identify abest option based on which option has the highest degree of motivation.

Where the derived degree of motivation of the identified best option isrelatively high, overall, and is higher than the degree of motivation ofeach of the other options by at least a predetermined threshold level,the identified best option may be presented to the decision maker as thebest option without any caveats. With such a high degree of motivationfor the best option, the decision maker can have confidence that theoption identified as the best option correctly reflects the applicationof both their instrumental and value rationality, presuming the inputthey provided is truthful.

However, where the degree of motivation for the best option is notrelatively high, overall, or is not higher than the degree of motivationfor one or more of the other options by at least the predeterminedthreshold level, then the identified best option may still be presentedto the decision maker as the best option, but with prompting for thedecision maker to further consider the current decision, includingproviding more information concerning the current decision and/orreconsidering the manner in which the decision maker has framed thecurrent decision. Such a lower degree of motivation associated with thebest option, either overall or in comparison to one or more of the otheroptions, may be very well be an indication that the decision maker hasnot considered the current decision well enough and/or long enough.Where the decision maker has used the system in the compact mode, suchprompting may include a suggestion that the decision maker switch tousing the expanded mode where the decision maker will at least be guidedthrough considering more details of the current decision. Where thedecision maker has already used the system in the expanded mode, suchprompting may include a suggestion that the decision maker again use theexpanded mode, but while also giving thought to re-framing the currentdecision, and/or the longer and/or shorter term goals associated withit.

As will be explained in greater detail, the system may store the inputreceived from the decision maker concerning a current decision in atree-like data structure. More specifically, the descriptive text thatdescribes the current decision and/or that describes the longer and/orshorter term goals may be stored in a manner associated with the base ofsuch a tree-like data structure. For each option, a separate branch ofsuch a data structure may be allocated to store the scale text selectedby the decision maker to indicate degrees of intensity and likelihoodassociated with that option, along with the descriptive text thatdescribes that option. Where the expanded mode is used such that thereare separate sets of degrees of intensity and likelihood for a possiblesuccessful outcome and for a possible unsuccessful outcome for eachoption, each branch of such a data structure may include a separatesub-branch allocated for each of the successful and unsuccessfulpossible outcomes at which may be stored the scale text selected by thedecision maker to indicate degrees of intensity and likelihood for thatoutcome, along with the descriptive text that describes that outcome.

Regardless of whether such information concerning a current decision isstored in a tree-like data structure or in some other form, as will beexplained in greater detail, various different embodiments of the systemmay support either or both of local and remote storage thereof to enablea decision maker to save their inputs of information concerning thecurrent decision. This may enable a decision maker to work incrementallywith the system through multiple sessions to make a current decisionover time, such that they may incrementally provide and/or refine theirinputs of information concerning the current decision over time.Alternatively or additionally, it may be that a decision maker seeks toexperiment with changing the options being considered in a currentdecision, and/or the degrees of intensity and/or likelihood associatedwith each option, to see how doing so may or may not change what isidentified as the best option. Also alternatively or additionally, itmay be that a decision maker seeks to try re-framing the currentdecision (possibly after being prompted to do so) such that they may trychanging how the current decision is described, and/or adding a longerand/or shorter term goals.

In some of such embodiments, the storage of differing versions of theinformation for a current decision may be supported. In this way, adecision maker may be provided with the ability to review previousversions of their inputs of information concerning the current decision,and/or to review previously derived best options, as part of consideringhow their thinking about the current decision may have evolved overtime. Alternatively or additionally, in this way, a decision maker maybe provided the option to return to an earlier version of their inputsconcerning a current decision if they come to the conclusion that alater version of their inputs represents a wrong direction concerningthe current decision. Alternatively or additionally, it may be changingcircumstances that cause a decision maker to reconsider a currentdecision such that the decision maker may re-frame it to fit the newcircumstances, and/or may alter their inputs concerning the currentdecision to take one or more factors of the changing circumstances intoaccount. It may be that the decision maker finds it useful to savemultiple versions of their inputs to enable further consideration of howthose inputs were changed in view of the changing circumstances.

In various embodiments, the ability to store inputs concerning a currentdecision may be used by a decision maker to share those inputs with anexpert in a subject that is touched upon by the current decision as partof asking questions and/or soliciting other input therefrom.Alternatively or additionally, an ability to share inputs concerning adecision may be used to enable shared decision making among multipledecision makers who need to cooperate in making a current decision.

In various embodiments, the system may store a set of templates forvarious types of decision that may need to be made by a decision maker.It may be that each such template is implemented as a data structure ofthe same type as is used for storing inputs of information from adecision maker concerning a current decision, but in which just a subsetof the inputs may already be provided as default values. By way ofexample, within such a data structure for a template, storage locationsfor a pre-determined quantity of branches may be pre-allocated thatcorrespond to the quantity of options to be considered. Also, withinsuch a template, descriptive text that describes the decision to bemade, that describes possible shorter and/or longer term goals, thatdescribes each option, and/or that describes possible successful andunsuccessful outcomes for each option may already be stored therein asdefault values that a decision maker can either except without change oredit. However, within such a template, scale text that indicates degreesof intensity and/or likelihood for each option, or for each possibleoutcome of each option, may deliberately not be provided to avoidinfluencing a decision maker.

In some embodiments, the system may be incorporated into a controlsystem of a vehicle (e.g., an airplane, train, ship, etc.) to assist inguiding crewmembers through making decisions concerning actions to takein response to an emergency that may arise (e.g., engine failure,control surface failure, hydraulics failure, compartment flooding,onboard fire, etc.). Such an embodiment of the system may providedecision templates that are each meant to frame a decision thatcurrently needs to be made in a manner that is intended to fit aparticular emergency situation, and may include options that are alsointended to fit a particular emergency situation. Such templates mayalso include references to particular portions of manuals that mayprovide more explanation of onboard systems and/or spell out variousemergency procedures. Similarly, in some embodiments, the system may bedeployed by emergency agencies of local, state/provincial and/ornational governments to assist emergency response personnel in makingdecisions concerning natural disasters or other situations related topublic safety. Again, decision templates may be provided that are eachmeant to frame a decision that currently needs to be made concerning aparticular type of public safety decision in which there may be a needto order evacuations, stay-at-home orders, curfews, closings oftransportation routes, etc.

It should also be noted that, in addition to, or in lieu of, using thesystem to assist in making a current decision, the system may be used toassist in evaluating a past decision that has already been made andacted upon. By way of example, the system may be used in a forensiccontext (e.g., in a post-accident investigation) to evaluate one or moredecisions made by a crewmember of an airplane, train or ship in responseto an emergency situation that may have confronted that crewmember. Insuch situations, there may be no time or opportunity for any suchcrewmember to use such a system to aid them in making decisionsconcerning what action to take. However, after the emergency is over, itmay be deemed valuable for purposes of an investigation to evaluate eachof the decisions made by such a crewmember to determine whether changesshould be made to training procedures, normal operating procedures, thedesign of the jetliner, ship, train, etc.

A decision making augmentation system includes: a manual input deviceconfigured to enable entry of text input by an operator of the decisionmaking augmentation system that describes aspects of a current decisioncomprising a selection of one option from among multiple options; adisplay configured to visually guide the operator through providing thetext input; a storage configured to store indications of the text input,wherein the text input comprises at least one of multiple descriptivetexts and multiple selections of scale text; and a processorcommunicatively coupled to at least the storage. The processor isconfigured to perform operations including: receive a decisiondescriptive text of the multiple descriptive texts, wherein the decisiondescriptive text describes the current decision; and cause repeatedpresentation of the decision descriptive text on the display. Theprocessor is also configured to, for each option of the multipleoptions, perform operations including: receive an indication of at leastone selection of scale text of the multiple selections of scale text,wherein the at least one selection of scale text specifies either adegree of intensity of seeking to achieve or avoid a possible outcome ofthe option, or a degree of likelihood of achieving or avoiding thepossible outcome of the option; and derive a degree of overallmotivation associated with the option based on the at least oneselection of scale text. The processor is further configured to:identify a best option from among the multiple options based on thedegree of overall motivation associated with each option; cause apresentation of an indication of the best option on the display; andcompare the degree of overall motivation associated with the best optionto a threshold degree of overall motivation. The processor is stillfurther configured to, in response to the degree of overall motivationassociated with the best option being less than the threshold degree ofoverall motivation, cause a presentation, on the display, of: a warningthat the degree of overall motivation associated with the best option islow; and a prompt for the operator to further consider the currentdecision. The processor is yet further configured to compare the degreeof overall motivation associated with the best option to the degree ofoverall motivation associated with each other option of the multipleoptions. The processor is also yet further configured to, in response tothe degree of overall motivation associated with the best option notexceeding, by at least a threshold degree of difference in overallmotivation, the degree of overall motivation associated with at leastone other option of the multiple options, cause a presentation, on thedisplay of: a proximity warning that the difference in degree of theoverall motivation associated with the best option from the overallmotivation associated with at least one other option is low; and theprompt for the operator to further consider the current decision.

A method of decision making augmentation includes: receiving, at aprocessor of a decision making augmentation system, and via a manualinput device configured to enable entry of text input by an operator, adecision descriptive text of multiple descriptive texts, wherein thedecision descriptive text describes a current decision comprising aselection of one option from among multiple options; and causingrepeated presentation of the decision descriptive text on a displayconfigured to visually guide the operator through providing the textinput, wherein the text input comprises at least one of the multipledescriptive texts and multiple selections of scale text. The method alsoincludes, for each option of the multiple options, performing operationsincluding: receiving, at the processor, and via the manual input device,an indication of at least one selection of scale text of the multipleselections of scale text, wherein the at least one selection of scaletext specifies either a degree of intensity of seeking to achieve oravoid a possible outcome of the option, or a degree of likelihood ofachieving or avoiding the possible outcome of the option; and deriving,by the processor, a degree of overall motivation associated with theoption based on the at least one selection of scale text. The methodfurther includes: identifying, by the processor, a best option fromamong the multiple options based on the degree of overall motivationassociated with each option; causing a presentation of an indication ofthe best option on the display; and comparing, by the processor, thedegree of overall motivation associated with the best option to athreshold degree of overall motivation. The method still furtherincludes, in response to the degree of overall motivation associatedwith the best option being less than the threshold degree of overallmotivation, causing a presentation, on the display, of: a warning thatthe degree of overall motivation associated with the best option is low;and a prompt for the operator to further consider the current decision.The method yet further includes comparing, by the processor, the degreeof overall motivation associated with the best option to the degree ofoverall motivation associated with each other option of the multipleoptions. The method also yet further includes, in response to the degreeof overall motivation associated with the best option not exceeding, byat least a threshold degree of difference in overall motivation, thedegree of overall motivation associated with at least one other optionof the multiple options, causing a presentation, on the display of: aproximity warning that the difference in degree of the overallmotivation associated with the best option from the overall motivationassociated with at least one other option is low; and the prompt for theoperator to further consider the current decision.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show aspects of alternate example implementations of adecision making augmentation system.

FIGS. 2A, 2B and 2C, together, show aspects of example data structuresused in the decision making augmentation system of either FIG. 1A or 1B.

FIGS. 3A, 3B, 3C, 3D, 3E, 3F, 3G, 3H, 3I, 3J, 3K, 3L and 3M, together,show aspects of an example performance of the decision makingaugmentation functionality of the decision making augmentation system ofeither FIG. 1A or 1B.

FIGS. 4A, 4B, 4C, 4D, 4E, 4F, 4G, 4H, 4I, 4J, 4K and 4L, together, showaspects of an example use of the decision making augmentationfunctionality of the decision making augmentation system of either FIG.1A or 1B in compact mode.

FIGS. 5A, 5B, 5C, 5D and 5E, together, show aspects of an example use ofthe decision making augmentation functionality of the decision makingaugmentation system of either FIG. 1A or 1B in expanded mode.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof. In the drawings, similarsymbols typically identify similar components, unless context dictatesotherwise. The illustrative embodiments described in the detaileddescription, drawings, and claims are not meant to be limiting. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the spirit or scope of the subject matter presentedherein. It will be readily understood that the aspects of the presentdisclosure, as generally described herein, and illustrated in theFigures, can be arranged, substituted, combined, separated, and designedin a wide variety of different configurations, all of which areexplicitly contemplated herein.

Disclosed herein is a decision making augmentation system thatimplements a method for both augmenting the short term memory of thebrain of a decision maker, and guiding the decision maker throughconsidering positive and negative aspects of each option of a currentdecision in a manner that integrates both instrumental rationality andvalue rationality based on the values of the decision maker.

A decision making augmentation system includes: a manual input deviceconfigured to enable entry of text input by an operator of the decisionmaking augmentation system that describes aspects of a current decisioncomprising a selection of one option from among multiple options; adisplay configured to visually guide the operator through providing thetext input; a storage configured to store indications of the text input,wherein the text input comprises at least one of multiple descriptivetexts and multiple selections of scale text; and a processorcommunicatively coupled to at least the storage. The processor isconfigured to perform operations including: receive a decisiondescriptive text of the multiple descriptive texts, wherein the decisiondescriptive text describes the current decision; and cause repeatedpresentation of the decision descriptive text on the display. Theprocessor is also configured to, for each option of the multipleoptions, perform operations including: receive an indication of at leastone selection of scale text of the multiple selections of scale text,wherein the at least one selection of scale text specifies either adegree of intensity of seeking to achieve or avoid a possible outcome ofthe option, or a degree of likelihood of achieving or avoiding thepossible outcome of the option; and derive a degree of overallmotivation associated with the option based on the at least oneselection of scale text. The processor is further configured to:identify a best option from among the multiple options based on thedegree of overall motivation associated with each option; cause apresentation of an indication of the best option on the display; andcompare the degree of overall motivation associated with the best optionto a threshold degree of overall motivation. The processor is stillfurther configured to, in response to the degree of overall motivationassociated with the best option being less than the threshold degree ofoverall motivation, cause a presentation, on the display, of: a warningthat the degree of overall motivation associated with the best option islow; and a prompt for the operator to further consider the currentdecision. The processor is yet further configured to compare the degreeof overall motivation associated with the best option to the degree ofoverall motivation associated with each other option of the multipleoptions. The processor is also yet further configured to, in response tothe degree of overall motivation associated with the best option notexceeding, by at least a threshold degree of difference in overallmotivation, the degree of overall motivation associated with at leastone other option of the multiple options, cause a presentation, on thedisplay of: a proximity warning that the difference in degree of theoverall motivation associated with the best option from the overallmotivation associated with at least one other option is low; and theprompt for the operator to further consider the current decision.

A method of decision making augmentation includes: receiving, at aprocessor of a decision making augmentation system, and via a manualinput device configured to enable entry of text input by an operator, adecision descriptive text of multiple descriptive texts, wherein thedecision descriptive text describes a current decision comprising aselection of one option from among multiple options; and causingrepeated presentation of the decision descriptive text on a displayconfigured to visually guide the operator through providing the textinput, wherein the text input comprises at least one of the multipledescriptive texts and multiple selections of scale text. The method alsoincludes, for each option of the multiple options, performing operationsincluding: receiving, at the processor, and via the manual input device,an indication of at least one selection of scale text of the multipleselections of scale text, wherein the at least one selection of scaletext specifies either a degree of intensity of seeking to achieve oravoid a possible outcome of the option, or a degree of likelihood ofachieving or avoiding the possible outcome of the option; and deriving,by the processor, a degree of overall motivation associated with theoption based on the at least one selection of scale text. The methodfurther includes: identifying, by the processor, a best option fromamong the multiple options based on the degree of overall motivationassociated with each option; causing a presentation of an indication ofthe best option on the display; and comparing, by the processor, thedegree of overall motivation associated with the best option to athreshold degree of overall motivation. The method still furtherincludes, in response to the degree of overall motivation associatedwith the best option being less than the threshold degree of overallmotivation, causing a presentation, on the display, of: a warning thatthe degree of overall motivation associated with the best option is low;and a prompt for the operator to further consider the current decision.The method yet further includes comparing, by the processor, the degreeof overall motivation associated with the best option to the degree ofoverall motivation associated with each other option of the multipleoptions. The method also yet further includes, in response to the degreeof overall motivation associated with the best option not exceeding, byat least a threshold degree of difference in overall motivation, thedegree of overall motivation associated with at least one other optionof the multiple options, causing a presentation, on the display of: aproximity warning that the difference in degree of the overallmotivation associated with the best option from the overall motivationassociated with at least one other option is low; and the prompt for theoperator to further consider the current decision.

FIG. 1A and 1B depict aspects of two different example embodiments of adecision making augmentation system 1000 that includes a storage device300, a processing device 500 and/or a processing device 700 coupled by anetwork 999 (e.g., cable-based and/or wireless links interconnectingdevices). In the decision making augmentation systems 1000 depicted ineach of FIGS. 1A and 1B, and as will be explained in greater detail, thedevices 300, 500 and/or 700 may cooperate through the network 999 toaugment the decision making capabilities of the brain of a decisionmaker in making a current decision. The processing device 700 maydirectly interact with a decision maker, providing a user interfaceincluding various prompts to guide the decision maker through providinginput concerning the current decision in a manner that elicits, and isindicative of, an integrated use of both instrumental and valuerationality faculties of the brain of the decision maker. In so doing,the processing device 700 may store such inputs in a decision frame 371,and the processing device 700 may recurringly present portions of thoseinputs to the decision maker in a manner that effectively augments theshort-term memory of the decision maker. The storage device 300 (ifpresent) may provide a set of decision templates 331 from which thedecision maker may be prompted to select a decision template 331 thatmost closely matches aspects of the current decision as part ofproviding input concerning the current decision. The storage device 300(if present) may additionally provide a set of decision aids to whichreferences (e.g., links) may be provided within the selected decisiontemplate 331. With inputs having been provided by the decision maker,either the processing device 700, or the processing device 500 depictedin FIG. 1A, may employ a subset of the inputs stored in the decisionframe 371 to derive degrees of motivation for each of the multipleoptions being considered in the current decision, and then uses thosedegrees of motivation to select one of those options as the best option.As will further be explained, the degrees of motivation may also beanalyzed to determine whether the decision maker should be prompted tofurther consider the current decision. Throughout, the decision frame371 of the current decision may be stored within the processing device700 to enable the decision maker to return to considering the currentdecision at a later time, and/or may be stored within the storage device300 (if present) to additionally enable the decision maker to shareaspects of current decision with others.

FIG. 1A depicts aspects of example embodiments of the system 1000 inwhich there is a division in processing operations between theprocessing device 700 that interacts directly with a decision maker andthe processing device 500 that may be more remotely located. Indeed, andalthough not specifically depicted, it may be that the single depictedprocessing device 500 cooperates with multiple ones of the processingdevice 700 to assist multiple decision makers in the making of multipledecisions.

As depicted, the storage device 300 (if present) may store an aiddatabase 310 of multiple decision aids 311, a template database 330 ofmultiple decision templates 331, and/or a decision database 370 ofmultiple decision frames 371. Each decision aid 311 may provide adecision maker with information concerning various aspects of thesubject of a current decision, and/or of an option that they may beconsidering as part of making a current decision. More specifically,each decision aid 311 may present information concerning variousadvantages and/or disadvantages concerning the subject of at least oneoption so as to provide a decision maker with information concerningwhat outcomes may be possible in connection with selecting one or moreoptions. In various embodiments, each of the decision aids may include atext document, still images, a slideshow, an audio/visual presentation,etc.

As previously discussed, each decision frame 371 may store the inputs ofa decision maker considering a current decision. In some embodiments,each decision frame 371 may correspond to a single decision to be made.However, and as will be explained in greater detail, it may be that, asa decision maker continues to consider a current decision (presumingthey have time to do so), the decision maker may provide multipleversions of their inputs concerning the current decision, and may desireto save each of those multiple versions to accommodate the possibilitythat an earlier version is later determined to represent a better coursein decision making than a later version. Thus, it may be that multipledecision frames 371, taken together as a set of versions, correspond toa single current decision.

As also previously discussed, each decision template 331 may store a setof pre-filled inputs for a type of decision that is to be made. Adecision maker may be provided with the option of selecting a decisiontemplate 331 from among the template database 330 that most closelyresembles the current decision that is to be made. As will be explainedin greater detail, the provision of decision templates 331 to decisionmakers may be deemed desirable as an aid in assisting the framing of acurrent decision and/or as an approach to providing references todecision aids 311.

As depicted, the processing device 500 includes a storage 560, one ormore processor(s) 550, and/or a network interface 590 to interface theprocessing device 550 with the network 999. The storage 560 stores acontrol routine 540, configuration data 510, an account database 570,the aid database 310 (or a copy thereof), the template database 330 (ora copy thereof), and/or the decision database 370 (or a copy thereof).The control routine 540 includes executable instructions operable on theprocessor(s) 550 to cause the processor(s) 550 to perform variousoperations. As will be explained in greater detail, the configurationdata 510 may include various parameters for the performance of thoseoperations such that the processor(s) 550 may be caused by execution ofthe control routine 540 to retrieve those parameters from theconfiguration data 510.

Similarly, the processing device 700 includes a storage 760, one or moreprocessor(s) 750, a display 780, at least one input device 720, and/or anetwork interface 790 to interface the processing device 750 with thenetwork 999. The storage 760 stores a control routine 740, one or moredecision aids 311 from the aid database 310, a decision template 331from the template database 330, and/or one or more decision frames 371that may be stored within the decision database 370. The control routine740 includes executable instructions operable on the processor(s) 750 tocause the processor(s) 750 to perform various operations.

In some embodiments, the majority of processing operations by which auser interface is generated, by which a decision maker is guided throughproviding input, by which an option in a current decision is selected,and by which an analysis of degrees of motivation occurs may beperformed by the processor(s) 550 of the processing device 500 as aresult of executing the control routine 540. In contrast, the processingoperations performed by the processor(s) 750 of the processing device700 as a result of executing the control routine 740 may be largelylimited to what might be called the role of a “dumb terminal” in whichprocessor(s) 750 are largely limited to operating the display 780 andthe input device(s) 720 to present that user interface to a decisionmaker.

An example of such embodiments may be where the processor(s) 550 of theprocessing device 500 are caused by execution of the control routine 540to provide a web server accessible via the network 999 to the processingdevice 700, and where the processor(s) 750 of the processing device 700are caused by execution of the control routine 740 to implement a webbrowser in which the display 780 and the input device(s) 720 areoperated to enable a decision maker to interact with web pages of webserver of the processing device 500 through the processing device 700.In this way, the user interface generated within the processing device500 is remotely provided to the decision maker through the processingdevice 700.

In other embodiments, the processing operations by which a userinterface is generated, by which a best option in a current decision isidentified, and by which an analysis of degrees of motivation occurs,may be more evenly divided between the processor(s) 550 of theprocessing device 500 and the processor(s) 750 of the processing device700. More specifically, it may be that the processor(s) 750 of theprocessing device 700 are caused by execution of the control routine 740to generate the user interface, to use the user interface to guide adecision maker through providing input concerning a current decision,and to store that input within a decision frame 371. Further, it may bethat the processor(s) 550 of the processing device 500 are caused byexecution of the control routine 540 to derive degrees of motivation forthe options of a current decision, to use the degrees of motivation toidentify a best option, and/or to analyze the degrees of motivation todetermine whether the decision maker should be prompted to furtherconsider the current decision.

In still other embodiments, a decision maker operating the processingdevice 700 may be given an option to specify the manner in which theperformance of processing operations may be divided between theprocessing devices 500 and 700. By way of example, the decision makermay be given the option to choose whether processing operationsassociated with making a current decision are divided between these twodevices (e.g., in what may be presented to the decision maker as an“online” mode), or having most (if not all) processing operationsperformed by the processor(s) 750 of the processing device 700 (e.g., inwhat may be presented as an “offline” mode) such that an ongoingconnection to the processing device 500 through the network 999 is notneeded. Such a choice between two such modes may also dictate whether asoftware routine (e.g., a copy of some or all of the control routine540) may be provided to and stored within the processing device 700 toenable execution thereof by the processor(s) 750.

Regardless of the exact manner in which the performances of suchprocessing operations are divided between the processing devices 500 and700, it may be that execution of the control routine 540 additionallycauses the processor(s) 550 of the processing device 500 to performvarious “gatekeeper” functions. More specifically, the processor(s) 550may be caused to control access to the aforedescribed functionality foraugmenting the decision making capabilities of a decision maker inmaking a current decision, and/or to control access to the databases310, 330 and/or 370, in a manner in which such access may be granted tojust selected decision makers. Such control over access may entail thestorage of security credentials (e.g., passwords, encryption keys,etc.), and/or other details associated with particular decision makersand/or associated with particular devices associated with particulardecision makers, within the account database 570. In embodiments inwhich the sharing of inputs of a decision maker with others is enabled,it may be that the security credentials, and/or other details associatedwith particular others, and/or associated with particular devicesassociated with particular others, area also stored within the accountdatabase 570.

FIG. 1B depicts aspects of example embodiments of the system 1000 inwhich the processing device 700 is operated in much more of a standaloneconfiguration, and the system 1000 may not include the processing device500. Thus, the processing device 700 may directly interact with thestorage device 300 (if present).

As depicted, the storage device 300 (if present) may include a storage360, a processor 350 and/or a network interface 390, and the storage 360may store the account database 570 and a control routine 340, inaddition to storing the aid database 310, the template database 330and/or the decision database 370. The control routine 340 includesexecutable instructions operable on the processor(s) 350 to cause theprocessor(s) 350 to perform various operations. In some embodiments,execution of the control routine 340 may cause the processor(s) 350 ofthe storage device 300 to take over the “gatekeeper” function that wasdescribed above as being performed by the processor(s) 550 of theprocessing device 500 in FIG. 1A.

In a manner similar to what was depicted in the processing device 700 ofFIG. 1A, the processing device 700 of FIB. 1B may also include thestorage 760, one or more processor(s) 750, the display 780, at least oneinput device 720, and/or the network interface 790 to interface theprocessing device 750 with the network 999. However, in addition to thestorage 760 storing the control routine 740, one or more decision aids311, a decision template 331, and/or one or more decision frames 371,the storage 760 may also store the control routine 540 and/or theconfiguration 510. Thus, both of the control routines 540 and 740 mayinclude executable instructions operable on the processor(s) 750 tocause the processor(s) 750 to perform various operations.

Referring now to both FIGS. 1A and 1B, each of the storages 360, 560 and760 may be based on any of a variety of volatile storage technologies,including and are not limited to, random-access memory (RAM), dynamicRAM (DRAM), Double-Data-Rate DRAM (DDR-DRAM), synchronous DRAM (SDRAM),static RAM (SRAM), etc.

Alternatively or additionally, the storage 360 may be based on any of avariety of non-volatile storage technologies.

Each of the processors 350, 550 and 750 may include any of a widevariety of processors, microcontrollers, gate-array logic devices, etc.that may be incorporate any of a variety of features to enhance speedand/or efficiency of processing operations. Such features may includeand are not limited to, multi-threading support per core component,multiple processing core components, directly integrated memory controlfunctionality, and/or various modes of operation by which speed ofthroughput and/or level of power consumption may be dynamically altered.

Each of the processors 350, 550 and 750 may be implemented as a singlesemiconductor die within a single package. Alternatively, each processor350 may be implemented as multiple semiconductor dies incorporated intoa single package, such as a multi-chip semiconductor package (e.g., asystem-on-a-chip, or SOC) in which the multiple semiconductor dies maybe interconnected in any of a variety of ways, including and not limitedto, conductive wires extending between adjacent semiconductor dies,and/or a substrate formed from multiple layers of conductors separatedby intervening layers of insulating material (e.g., a printed circuitboard, or PCB) onto which the multiple semiconductor dies may besoldered.

Each of the network interfaces 390, 590 and 790 may employ any of avariety of wireless communications technologies, including and notlimited to, radio frequency transmission, transmission incorporated intoelectromagnetic fields by which electric power may be wirelesslyconveyed, and/or any of a variety of types of optical transmission.Additionally, each of the network interfaces 390, 590 and 790 may beconfigured to engage in communications that adhere in timings, protocoland/or in other aspects to one or more known and widely used standards,including and not limited to IEEE 802.11a, 802.11ad, 802.11ah, 802.11ax,802.11b, 802.11g, 802.16, 802.20 (commonly referred to as “MobileBroadband Wireless Access”); Bluetooth; ZigBee; or a cellularradiotelephone service such as GSM with General Packet Radio Service(GSM/GPRS), CDMA/1×RTT, Enhanced Data Rates for Global Evolution (EDGE),Evolution Data Only/Optimized (EV-DO), Evolution For Data and Voice(EV-DV), High Speed Downlink Packet Access (HSDPA), High Speed UplinkPacket Access (HSUPA), 4G LTE, etc.

FIGS. 2A, 2B and 2C, together, depict aspects of example embodiments ofdata structures employed by various embodiments of the decision makingaugmentation system 1000 of either of FIG. 1A or 1B. More specifically,FIGS. 2A and 2B depict aspects of two different embodiments of thedecision frame 371 associated with the operation of system 1000 ineither of the compact mode (i.e., a decision frame 371 c) or theexpanded mode (i.e., a decision frame 371 e). FIG. 2C depicts aspects ofthe manner in which a decision template 331 may be used as a basis forthe generation of a decision frame 371 (associated with either of thecompact or expanded modes), along with other inputs provided by adecision maker.

Turning to FIGS. 2A-B, as previously discussed, the system 1000 may beoperated in either of a compact mode or an expanded mode. Again, aspreviously discussed, the compact mode may be better suited forsituations in which a current decision must be made in a relativelyshort period of time such that a decision maker is guided throughproviding a reduced set of inputs about the current decision. As alsopreviously discussed, the expanded mode may be better suited forsituations in which there is more time to more carefully consider acurrent decision such that a decision maker is guided through providinga larger set of inputs about the current decision, including shorterand/or longer term goals.

FIG. 2A depicts aspects of the contents and organization of an exampledecision frame 371 c generated during operation of the system 1000 inthe compact mode. As depicted, the decision frame 371 c may be a datastructure with a tree-like organization of items of data therein, wherethe tree-like organization is meant to correspond to various aspects ofa decision that is to be made. More specifically, a separate branch maybe allocated directly off the root of the tree for each option of themultiple options that are being considered as part of making thedecision. It should be noted that there is a presumption that eachdecision for which the system 1000 is used will essentially be a choicefrom among at least two options.

While the system 1000 is operated in the compact mode, a decision makermay be guided through entering descriptive text (e.g., via the userinterface provided using the input device(s) 720 and the display 780)that describes the current decision, and that describes each of theoptions that is being considered. The descriptive text that describesthe current decision may be stored within the decision frame 371 c asthe decision root text 373, and the descriptive text that describes eachone of the options may be stored as the option branch text 374 of thebranch that corresponds to that option.

As will be explained in greater detail, the descriptive text that isstored as the decision root text 373 and as each of the option branchtexts 374 may not be used, at all, in the derivation of degrees ofmotivation, in identifying a best option based on the degrees ofmotivation, or in analyzing the degrees of motivation to determinewhether to the decision maker is to be prompted to further consider thecurrent decision. Instead, the descriptive text that is stored as thedecision root text 373, and as each of the option branch texts 374, maybe repeatedly visually presented to the decision maker through the userinterface that is provided using the display 780 and the input devices720 as part of an approach to augmenting the limited capacity of theshort-term memory, and to reducing the need for accesses to long-termmemory. Stated differently, such text may be repeatedly visuallypresented to the decision maker to better enable use of the short-termterm memory of the decision maker's brain to retain other informationthat is pertinent to making the current decision.

In addition to the decision maker being guided through entering suchdescriptive text, the decision maker may also be guided throughselecting scale texts from menus as an approach to providing input thatis indicative of degrees of intensity (I) and likelihood (L) that areassociated by the decision maker with each option. Indications of theselections of scale texts made by the decision maker for each option maybe stored as an IL frame 378. As will be explained in greater detail,for the decision frame 371 c, it may be the indications stored in the ILframes 378 of selections of scale text indicative of degrees ofintensity and likelihood for each option that are used to derive degreesof motivation for the options, which are then used to identify one ofthe options as the best option, and which are also analyzed to determinewhether a decision maker should be prompted to further consider thecurrent decision.

FIG. 2B depicts aspects of the contents and organization of an exampledecision frame 371 e generated during operation of the system 1000 inthe expanded mode. As depicted, and like the decision frame 371 c ofFIG. 2A associated with the compact mode, the decision frame 371 e ofFIG. 2B associated with the expanded mode may also be a data structurewith a tree-like organization of items of data therein, where thetree-like organization is again meant to correspond to various aspectsof a current decision. Indeed, as will be discussed in greater detail, adecision frame 371 c generated during the compact mode may be convertedinto a decision frame 371 e as part of a change from use of the compactmode for a current decision to use of the expanded mode for that samecurrent decision. Again, a separate branch of the tree-like organizationmay be allocated directly off the root of the tree for each option thatis being considered in a current decision. However, unlike the tree-likeorganization of the decision frame 371 c, each branch in the tree-likeorganization of the decision frame 371 e that is associated with anoption includes a pair of sub-branches, where one sub-branch isassociated with a possible successful outcome of the option and theother sub-branch is associated with a possible unsuccessful outcome ofthe option. Also unlike the tree-like organization of the decision frame371 c in which a single separate IL-frame 378 is associated with eachbranch for an option, in the tree-like organization of the decisionframe 371 e, separate IL-frames 378 s and 378 u are associated with thesub-branches for successful and unsuccessful outcomes, respectively, foreach option.

While the system 1000 is operated in the expanded mode, in addition tobeing guided through entering descriptive text that describes thecurrent decision and each of the options (as is the case in the compactmode), the decision maker is also guided through entering descriptivetext that describes shorter and/or longer term goal(s) associated withthe current decision, and that describes each possible successful andunsuccessful outcome associated with each option. Somewhat similar tothe decision frame 371 c, in the decision frame 371 e, the descriptivetext that describes the shorter and/or longer term goal(s) may bestored, along with the descriptive text the describes the currentdecision, as the decision root text 373. Also, as was the case with thedecision frame 371 c, in the decision frame 371 e, the descriptive textthat describes each one of the options may be stored as the optionbranch text 374 of the branch that corresponds to that option. However,in the decision frame 371 e, additionally for each option, thedescriptive text that describes each possible successful andunsuccessful outcome of each option may be stored as the optionsuccessful sub-branch text 377 s and the option unsuccessful sub-branchtext 377 u, respectively.

Instead of the decision maker being guided through selecting scale textsfrom menus that are indicative of degrees of intensity (I) andlikelihood (L) that are associated by the decision maker with positiveand negative aspects of each option, as was the case in compact mode, inexpanded mode, the decision maker may be guided through selecting scaletexts from menus that are indicative of degrees of intensity (I) andlikelihood (L) that are separately associated by the decision maker withpositive and negative aspects of each of the possible successful andunsuccessful outcomes associated with each option. Indications of suchselections of scale text made by the decision maker for the successfuloutcomes of each option may be stored as the IL frame 378 s for eachoption, while the indications of such selections of scale text made bythe decision maker for the unsuccessful outcomes of each option may bestored as the IL frame 378 u for each option. As will be explained ingreater detail, for the decision frame 371 e, it may be the indicationsof selections of scale text that are indicative of degrees of intensityand likelihood stored as the IL selections 378 s and 378 u for eachoption that are used to derive degrees of motivation for each option,which are then used to identify a best option, and which are analyzed todetermine whether the decision maker should be prompted to furtherconsider the current decision.

FIG. 2C depicts aspects of the manner in which a decision frame 371 (ofeither of the types 371 c or 371 e associated with the compact orexpanded modes, respectively) may be partially generated from a decisiontemplate 331. As previously discussed, in embodiments of the decisionmaking augmentation system 1000 that include the ability to providedecision templates 331, a decision maker may at least be given theoption of selecting a decision template 331 having options and/or otherfeatures of a decision that match the current decision.

As depicted, the decision template 331 may have just a subset of thecontents of the decision frame 371 that is to be generated from thedecision template 331. More specifically, the decision template 331 mayinclude just descriptive text that would otherwise be directly enteredby a decision maker to describe various aspects of a current decision,and may not include any indication of any selection of scale text thatwould indicate a degree of intensity or likelihood for any option or anypossible outcome of any option. Such a lack of provision of selectionsof scale text in the decision template(s) 331 may be deemed desirable toavoid any possibility of influencing a decision maker in specifying anydegree of intensity or likelihood. In other words, it may be deemeddesirable to require that a decision maker select scale texts indicatingdegrees of likelihood and intensity without any hint or suggestion ofselections in a decision template 331.

As depicted, any descriptive text that may be stored as the templatedecision root text 333 within the decision template 331 (i.e.,descriptive text that may describe a decision, and/or shorter and/orlonger term goals) may become the default descriptive text that isstored as the decision root text 373 within a decision frame 371.Similarly, for each option, any descriptive text that may be stored asthe corresponding template option branch text 334 (i.e., descriptivetext that may describe that option) may become the default descriptivetext that is stored as the corresponding option branch text 374 for thatoption within the decision frame 371. Also similarly, for eachsuccessful or unsuccessful outcome for an option, any descriptive textthat may be stored as the corresponding template option successfulsub-branch text 337 s or template option unsuccessful sub-branch text337 u (i.e., descriptive text that may describe that outcome) may becomethe default descriptive text that is stored as the corresponding optionsuccessful sub-branch text 377 s or option unsuccessful sub-branch text377 u, respectively, for that outcome within the decision frame 371.

Again, it should be noted that such descriptive texts as may be storedwithin the decision template 331 stored as texts 333, 334, 337 s and/or337 u are intended to serve merely as default descriptive texts forstorage within the decision frame 371 as texts 373, 374, 377 s and/or377 u, respectively. Thus, after being so stored within the decisionframe 371, a decision maker may be guided through a series ofopportunities to edit such default descriptive texts to better reflectwording that may be more familiar to the decision maker and/or as partof re-framing the current decision in a manner that differs from how itwas framed in the decision template 331.

As also depicted, one or more of such descriptive texts associated withthe current decision, with each option, and/or with each possibleoutcome of each option, that is stored within a decision template 331may include references to one or more decision aids 311. In this way, inaddition to providing a decision maker with a default descriptive texts,the decision maker may also be provided with suggestions for whatdecision aids 311 to consult as part of gathering information needed toconsider the options of the current decision. In some embodiments, suchreferences may be implemented as selectable links implemented as uniformresource locators (URLs) that enable access to decision aids 311 thatmay be made accessible at web pages.

It should be noted that, in depicting a decision frame 371 in FIG. 2C toencompass either the decision frame 371 c introduced in FIG. 2A andassociated with the compact mode or the decision frame 373 e introducedin FIG. 2B and associated with the expanded mode, both the single ILframe 378 of the compact mode and the pair of IL frames 378 s and 378 uare depicted. It is not envisioned that a decision frame 371 would everinclude both the single IL frame 378 associated with each option, andthe pair of IL frames 378 s and 378 u associated with the pair of both asuccessful outcome and an unsuccessful outcome of an option. Instead, itis envisioned that, for each option of a current decision, thecorresponding decision frame 371 would include either the single ILframe 378 or the pair of IL frames 378 s and 378 u.

FIGS. 3A through 3M, together, depict aspects of an example performanceof the decision making augmentation function of embodiments of thedecision making augmentation system 1000 of either of FIG. 1A or 1B.More specifically, FIG. 3A depicts aspects of the allocation of portionsof the decision making augmentation function among devices 300, 500and/or 700 of the system 1000. FIGS. 3B-F, together, depict aspects ofthe system 1000 guiding a decision maker through providing inputsconcerning a current decision. FIGS. 3G-M, together, depict aspects ofthe system 1000 deriving degrees of motivation for multiple options of acurrent decision, identifying a best option from among the multipleoptions, and/or presenting the decision maker with indications of thebest option and/or of a need to further consider the current decision.

Turning to FIG. 3A, as previously discussed in reference to FIGS. 1A-B,there is a wide variety of ways in which portions of the functionalityof the system 1000 may be allocated among the devices 300, 500 and/or700. Accordingly, there is a wide variety of ways in which the devices300, 500 and/or 700 may cooperate to augment the decision makingfunctionality of the brain of a decision maker operating the processingdevice 700. And accordingly, there is a wide variety of ways in whichparticular components of executable instructions may be allocated amongthe control routines 340, 540 and/or 740.

As depicted, the control routine 740 may incorporate a UI component 747that is executable by the processor(s) 750 of the processing device 700to cause the processor(s) 750 to operate the display 780 and the inputdevice 720 together to provide a user interface. It may be that thecontrol routine 740 also incorporates an interaction component 746 thatis executable by the processor(s) 750 to cause the processor(s) 750 togenerate the visual prompts that may be presented to a decision makerthrough the user interface to provide such guidance, and to cause theprocessor(s) 750 to store the inputs received through that userinterface as portions of a decision frame 371.

Alternatively, it may also be that the control routine 540 incorporatesan interaction component 546 that is executable by the processor(s) 550of the processing device 500 to cause the processor(s) 550 to generatethe visual prompts that may be presented to a decision maker through theuser interface to guide the decision maker through providing inputconcerning a current decision, and to cause the processor(s) 550 tostore the inputs received through that user interface as portions of adecision frame 371.

As depicted, the control routine 540 may incorporate a derivationcomponent 544 that is executable by the processor(s) 550 of theprocessing device 500 to cause the processor(s) 550 to use inputindications of degrees of intensity and likelihood associated with eachoption of a current decision to derive degrees of motivation for eachoption, to use the degrees of motivation to identify a best option fromamong multiple options of a current decision, and/or to use the degreesof motivation to determine whether a decision maker should be promptedto further consider the current decision. The control routine 540 mayalso incorporate a control component 545 that is executable by theprocessor(s) 550 to control the performance of the decision makingaugmentation function of the other components of the control routines340, 540 and/or 740 of the system 1000. It may be that the controlroutine 540 also incorporates an access component 543 that is executableby the processor(s) 550 to cause the processor(s) 550 to control accessto the databases 310, 330 and/or 370, and/or to control access to thedecision making augmentation functionality of the system 1000.

Alternatively, it may also be that the control routine 340 incorporatesan access component 343 that is executable by the processor(s) 350 ofthe storage device 300 to cause the processor(s) 350 to control accessto the databases 310, 330 and/or 370, and/or to control access to thedecision making augmentation functionality of the system 1000.

It should be noted that, as previously discussed in reference to FIGS.1A-B, it may be that the control routine 540 is caused to be executed byprocessor(s) 750 of the processing device 700, instead of byprocessor(s) 550 of the processing device 500. Thus, in someembodiments, it may be that the processor(s) 750 of the processingdevice 700 are caused to execute the components of both of the controlroutines 740 and 540.

Turning to FIG. 3B, execution of the control component 545 by theprocessor(s) 550 or 750 to control the performance of the decisionmaking functionality may cause commencement thereof to begin with theauthentication of a decision maker, selection of a decision template331, and/or selection of decision aids 311. Thus, in executing theaccess component 543 or 343, the processor(s) 550 or 350, respectively,may be caused to enforce security by employing security credentials(e.g., password, fingerprint, voiceprint, etc.) provided by a decisionmaker to selectively allow access to the databases 310, 330 and/or 370.In support of such enforcement of security, the processor(s) 750 or 550may be caused by execution of the interaction component 746 or 546,respectively, to generate one or more prompts for being presented to thedecision maker to provide such security credentials. In executing the UIcomponent 747, those one or more prompts may be caused to be sopresented by the processor(s) 750 as part of operating the display 780and the input device 720 to present a user interface to the decisionmaker.

As previously discussed in reference to FIG. 2C, it may be that one ormore decision templates 331 are made available for being selected by thedecision maker as an approach to assisting the decision maker in framinga current decision by providing default versions of descriptive textthat the decision maker may use without modification and/or may edit.More specifically, such default versions of descriptive text may providea default framing of what the current decision is, what the shorterand/or longer term goals are (if shorter and/or longer term goals are tobe explicitly specified), what each of the options are, and/or what thepossible successful and unsuccessful outcomes are for each of theoptions are (if the possible outcomes are to be explicitly specified).In support of making available one or more decision templates 331 forbeing selected by the decision maker (if decision templates 331 are tobe made available), the processor(s) 750 or 550 may be caused byexecution of the interaction component 746 or 546, respectively, togenerate one or more prompts for being presented to the decision makervia the UI to guide the decision maker through selecting a decisiontemplate 331.

In some embodiments, as part of prompting the decision maker to providesecurity credentials and/or to select a decision template 331, thedecision maker may also be prompted to specify whether to use thedecision making augmentation system 1000 in one of the previouslydiscussed compact mode or expanded mode based on the decision maker'sassessment of the amount of time that the decision maker has availablein which to consider the current decision. As has been discussed (atleast in reference to FIGS. 2A-B), use of the expanded mode may bedeemed advantageous as the decision maker is then prompted throughproviding more detailed input (and/or to edit more detailed defaultinput provided in a decision template 331) to thereby explicitly specifymore aspects of the current decision, thereby causing the decision makerto more carefully consider the best way in which to frame the decisionand/or the options thereof. However, as has also been discussed, thedecision maker may not have sufficient time available in which to makethe current decision, in which case the less detailed approach of thecompact mode may prove sufficient in support of considering the currentdecision.

As has been discussed, in some embodiments, a selected decision template331 may include references to one or more decision aids 311 as part ofmaking available, to the decision maker, decision aids 311 that maycontain information concerning one or more subjects associated with thecurrent decision, and/or associated with a particular option of thecurrent decision. Again, such references may be included in the defaultdescriptive text provided in the decision template 331 for the decisionas a whole and/or for one or more of the options. Also again, suchreferences may include selectable links that trigger the provision andpresentation of images of the decision aids 311 (e.g., URLs to webpages). In this way, the decision maker may be guided through learningaspects of such subjects as the decision maker considers the currentdecision as a whole, and/or as the decision maker is guided throughconsidering each option, individually.

Turning to FIG. 3C, continued execution of the control component 545 maycause the performance of the decision making functionality of the system1000 to continue with the decision maker being prompted to enter and/oredit descriptive text, and for the descriptive text to be stored as partof a decision frame 371 generated for current decision. Thus, inexecuting the interaction component 546 or 746, the processor(s) 550 or750, respectively, may be caused to generate one or more prompts forbeing presented via the user interface to the decision maker to enterdescriptive text, and/or to edit descriptive text, that explicitlydescribes the current decision, and/or shorter and/or longer term goals(in the expanded mode). Following the provision of such descriptive text(either through direct entry or through selection of a decision template331) by the decision maker, such descriptive text that describes thecurrent decision, and/or shorter and/or longer term goals may be storedwithin the depicted decision frame 371 as the decision root text 373.

Similarly, in continuing to execute the interaction component 546 or746, the processor(s) 550 or 750, respectively, may be caused togenerate one or more prompts for being presented to the decision makerto enter and/or edit descriptive text, that describes each of themultiple options of the current decision and/or that describes thepossible successful and unsuccessful outcomes of each option (in theexpanded mode). Following the provision of such descriptive text (again,either through direct entry or through selection of a decision template331) by the decision maker, the descriptive text that describes eachoption may be stored as a corresponding instance of the option branchtext 374. Similarly, in the expanded mode, the descriptive text thatdescribes each possible successful outcome of each option may be storedas a corresponding instance of the option successful sub-branch text 377s, and the descriptive text that describes each possible unsuccessfuloutcome of each option may be stored as a corresponding instance of theoption unsuccessful sub-branch text 377 u.

Following the input and storage of such descriptive texts, continuedexecution of the interaction component 546 or 746 may cause theprocessor(s) 550 or 750, respectively, to generate visual presentationsof at least a subset of those descriptive texts stored within thedecision frame 371. Such visual presentations of descriptive texts maybe repeatedly presented on the display 780 as part of the user interfaceprovided by the UI component 747. As has been discussed, such a repeatedpresentation of descriptive texts on the display 780 may be part of anapproach to effectively augmenting the short-term memory of the brain ofthe decision maker. Stated differently, having guided the decision makerthrough entering such descriptive text, which would have engaged use ofshort term memory, the repeated visual presentation of those descriptivetexts on the display 780 may make such descriptive texts readilyavailable for re-reading to enough of a degree that the need to continueto retain them in short-term memory is at least reduced, if notobviated.

As also previously discussed, chunks of new information that areretained in the short term memory of the human brain may also besubsequently retained in longer term memory as part of beginning tobuild associations between such new information and other informationalready previously retained in the long-term memory. As will also befamiliar to those skilled in the art, accessing information retained inlong-term memory tends to require more time and effort than informationretained in short-term memory. The amount of time and effort to accessinformation retained in long-term memory does tend to be reduced if moreassociations are made that connect that information to other informationsuch that there are more pathways of association in place for enablingsuch association-based access. However, the repeated visual presentationof at least some of the descriptive texts on the display 780 also freesthe brain of the decision maker from having to expend the time andeffort to access that information, which may be sufficiently new as tohave been retained in long-term memory with relatively few associationsyet in place.

With the brain of the decision maker having been freed from continuingto use short-term memory to retain the descriptive texts, and with thebrain of the decision maker having been freed of the necessity of takingthe time and effort to access the descriptive texts in long-term memory,the short-term memory and other resources of the brain of the decisionmaker are made more readily available to better enable consideration ofother aspects of the current decision, such as new information that maybe provided by one or more decision aids. Additionally, the brain of thedecision maker is able to more easily transition to being guided throughconsidering the advantages and disadvantages of each of the multipleoptions of the current decision, as will shortly be explained.

Turning to FIG. 3D, continued execution of the control component 545 maycause the performance of the decision making functionality of the system1000 to continue with the decision maker being prompted to select scaletexts from menus of multiple scale texts that are each textuallydescriptive of degrees of intensity and likelihood, and for indicationsof those scale text selections to be stored as part of the depicteddecision frame 371 generated for the current decision. Thus, inexecuting the interaction component 546 or 746, the processor(s) 550 or750, respectively, may be caused to generate prompts and correspondingimages of menus of scale texts for being presented via the userinterface to the decision maker. As the decision maker makes selectionsof scale texts from such menus, indications of such selections for eachoption may be stored within a single IL frame 378 allocated for eachoption in the compact mode, or indications of such selections for thepossible successful outcome and for the possible unsuccessful outcome ofeach option may be stored in separate corresponding IL frames 378 s and378 u, respectively.

Turning to FIG. 3E, continued execution of the control component 545 maycause the provision, throughout the performance of the decision makingaugmentation functionality of the system 1000, of the option to save thecontents of the decision frame 371 in persistent storage within thedecision database 370 and/or to share the contents of the decision frame371 with others. As previously discussed, a decision maker may interactwith the system 1000 concerning a current decision in multiple separatesessions over a period of time, especially where the decision maker doeshave the time to more carefully consider the current decision. Thus,between such separated sessions, the decision maker may use the userinterface to provide a command to save (persistently store) the decisionframe 371 so that the decision maker may subsequently retrieve it frompersistent storage when ready to return to considering the currentdecision. Such a command may be conveyed, along with the decision frame371, itself, to where the decision database 370 may be stored within theprocessing device 500 and/or the storage device 300.

As also previously discussed, a decision maker may so save the decisionframe 371 in such persistent storage as part of sharing the contentsthereof with one or more others. Again, it may be that the currentdecision is meant to be a shared decision where there is meant to bemore one decision maker involved, or at least one or more others who aremeant to contribute information and/or opinions concerning the currentdecision to the decision maker. Alternatively or additionally, thedecision maker may share the contents of the decision frame 371 with oneor more others who have expertise in a subject associated with thecurrent decision so as to elicit information and/or opinions therefrom.In some embodiments, additional storage space (not shown) within thedecision frame 371 may be allocated to store text and/or other types ofinformation that may be stored therein by such others for the purpose ofbeing considered by the decision maker. Alternatively or additionally,such others may be permitted to at least augment the descriptive textprovided by the decision maker with references to one or more decisionaids 311 for the benefit of the decision maker. Thus, where others areaware of decision aids 311 that may be of benefit to the decision makerin considering the current decision as a whole, and/or in consideringaspects of one or more particular options thereof, such others may bepermitted to augment the descriptive text that describes the currentdecision, and/or that describes one or more particular options, withreferences to such decision aids 311.

Where the decision frame 371 is persistently stored as part of enablingit to be shared, it may be that the command to do so that is provided bythe decision maker includes identifiers of the one or more others withwhich the contents of the decision frame 371 are to be shared. In someembodiments, continued execution of the access component 343 or 543 bythe processor(s) 350 or 550, respectively, may cause the contents of theaccount database 570 that are associated with the decision maker to beupdated with such identifiers of the one or more others to enable thoseaccess to the decision frame 371 to be granted to those one or moreothers.

Turning to FIG. 3F, regardless of whether a decision maker haspersistently stored and/or shared the decision frame 371 of a currentdecision with others, and regardless of whether the decision makercompletes the entry/editing of descriptive texts and/or completes theselection of scale texts indicative of intensity and likelihood in asingle session or across multiple sessions of interacting with thesystem 1000, the decision maker may use the user interface to provide acommand to the system 1000 to use their inputs to at least derivedegrees of motivation for one or more of the options, if not also usethe derived degrees of motivation to identify a best option. Furtherexecution of the control component 545 may cause execution of thederivation component 544 by the processor(s) 550 or 750 in response tosuch a command. More specifically, and as depicted, the derivationcomponent may incorporate executable instructions for the performance ofa derivation function 544 d to generate degrees of motivation fromdegrees of intensity and likelihood, an aggregation function 544 a tointegrate multiple degrees of motivation associated with a singleoption, and/or a comparator function 544 c to compare degrees ofmotivation associated with the multiple options to identify a bestoption.

FIGS. 3G-H, together, depict various aspects of the derivation function544 d. Through a series of experiments conducted over many years byMikhail Kotik (the father of the inventor, Alexander Yemelyanov, of thepresent application) with individuals involved in making decisionsacross a wide variety of industries, a number of insights were gainedinto the manner in which the human brain makes decisions. At first, suchexperiments were conducted in a manner focusing on the potentialnegative outcomes of decisions in industries where those outcomes couldeasily include injury or death (e.g., pilots, police officers,construction workers, etc.). Later experiments were conducted to alsoinclude potential positive outcomes in industries and other areas wherethere was more of a balance in potential positive and negative outcomesof decisions. The insights gained from such experiments include theidentification of what questions to ask and how to ask those questionsto elicit the information needed to further develop an understanding ofthe manner in which the human brain considers aspects of a choice fromamong multiple options as part of making a decision.

Among the questions that were found to be the most useful to ask werequestions concerning degrees of intensity and likelihood. Intensityrefers to the intensity with which a decision maker seeks to achieve aparticular positive outcome, or the intensity with which a decisionmaker seeks to avoid a particular negative outcome. Regarding a positiveoutcome, likelihood refers to the perception that a decision maker hasof how likely it is that the positive outcome can be achieved, takinginto account difficulties that may need to be overcome vs. the abilityand/or willingness of the decision maker to make an effort to overcomethose obstacles. Regarding a negative outcome, likelihood refers to theperception that a decision maker has of how likely it is that thenegative outcome can be avoided, taking into account obstacles that mayneed to be overcome vs. the ability and/or willingness of the decisionmaker to make an effort to overcome those obstacles.

Regarding how to ask such questions, Kotik determined that asking for anumeric characterization (e.g., asking that a number on a scale of 1 to10 be specified) of either intensity or likelihood did not yield answersthat proved to be as accurate or consistent as asking that a degree bespecified by selecting scale text from among a selection of scale textsthat, together, define a scale of degrees of intensity or a scale ofdegrees of likelihood without the use of numbers. By way of example, andas depicted, a degree of intensity may be specified by selecting fromamong pieces of text such as: “extremely weak”, “very weak”, “weak”,“not weak—not strong”, “strong”, “very strong” and “extremely strong”.More specifically, from such experimentation, it was discovered that,when a decision maker was required to specify such a degree by selectingscale text from a selection of scale texts, there was a strongertendency toward achieving better scaling.

Extrapolating from other studies about which portions of the brain tendto be used in thinking about a subject expressed in different ways(e.g., with a picture vs. words vs. numbers), it appears that differentportions of the brain are engaged when an aspect of a subject is to beexpressed using words vs. using numbers. Advantageously, the requirementto express a degree of something with words, instead of with numbers,generally resulted in more consistent expressions of degree referencedto a scale. In particular, a form of cross-contamination seems to betriggered by the use of numbers in which there is a tendency toattribute a unit of measure to the numbers (e.g., a measure of an amountof money, weight, distance, time, etc.) that would disrupt the abilityto conceive of degrees of intensity or likelihood as being referenced toa given numeric scale such that the resulting answers concerning degreesof intensity or likelihood would be effectively contaminated by theexternal influence of a unit of measure. Stated differently, elicitingindications of degrees of intensity or likelihood from decision makersusing a scale expressed with numbers tended to result in the brainslipping into a form of “counting mode” that invited such contaminationof thought, instead of leading the brain into a form of “measuring mode”that proved significantly less susceptible to such contamination. Ineffect, the use of words encouraged the brains of decision makers toreliably engage in measuring within the scale that was provided to them,and to avoid slipping into counting in a manner that ignored any givenscale.

From analyzing the results of his experiments to identify correlations,Kotik determined that there was a reliably repeatable relationship amongdegrees of intensity, likelihood and motivation that could begraphically plotted, as shown in FIG. 3G. Each of the depictednon-linear gradations (i.e., each of the depicted curving lines) of“very high”, “high”, “middle”, “low” and “very low” describes a degreeof motivation. Motivation refers to the motivation of a decision makerto make the effort to do what must be done to overcome obstacles toachieve a particular positive outcome, or the motivation of a decisionmaker to make the effort to do what must be done to overcome obstaclesto avoid a particular negative outcome. From analyzing the results ofhis experiments, Kotik also determined that the same relationship amongdegrees of intensity, likelihood and motivation applies regardless ofwhether the outcome is a positive outcome or a negative outcome, andregardless of whether physical, social or material nature of thepositive outcome or the negative outcome, such that the graphical plotof this relationship shown in FIG. 3G applies to either positive ornegative outcomes.

As is also shown in FIG. 3G, although asking decision makers to specifyeither a degree of intensity or a degree of likelihood using numbersreferenced to a numeric scale proved problematic, as described justabove, the selections of words that the decision makers were required touse (i.e., the selections of scale texts presented in menus) are able tobe subsequently correlated to numeric values on corresponding numericscales (e.g., the depicted ranges of numeric values 1-7 correlated, asshown, to the scale texts along the horizontal and vertical axes). Inthis way, the degrees of intensity and likelihood expressed by decisionmakers through the selections of scale texts from menus are able to beused as inputs to derive degrees of motivation.

Beyond revealing a reliable relationship among degrees of intensity,likelihood and motivation, the increased reliability and consistency ofresults achieved through such use of words (i.e., scale texts) toexpress degrees of intensity and likelihood also enable an understandingof the manner in which perception of information on the part of adecision maker influences the degrees of intensity and likelihood. Whileinformation concerning subject(s) associated with a decision is clearlyan input to decision making, it has been found that it is actually theperception of that information on the part of the decision maker that isthe actual input, and not the information in its pure form. Theperception of information on the part of a decision maker is necessarilyinfluenced by the values of the decision maker, and by the pastexperiences of a decision maker, since it is the values and pastexperiences of the decision maker that dictate how a decision makerreacts to any piece of information (e.g., whether a piece of informationdescribes something good or bad, and to what degree of goodness andbadness). Thus, the perception of information of a decision maker mayweave together internal information of the decision maker (e.g., pastexperiences) and new information provided to the decision maker from anexternal source (e.g., current circumstances in which a decision is tobe made).

Thus, a degree of intensity ascribed by a decision maker to an outcomeof an option is necessarily driven by the decision maker's perception ofinformation associated with how an option could lead to that outcome.Correspondingly, a degree of likelihood in achieving or avoiding thatoutcome is necessarily driven by the decision maker's perception of theobstacles to be overcome in doing so vs. the ability/willingness of thedecision maker to do what must be done to achieve or avoid that outcome.The perception that a decision maker has of various obstacles isnecessarily influenced, at least in part, by their past experiences,including past experiences in overcoming obstacles.

It is in this way that asking a decision maker to express degrees ofintensity and likelihood actually elicit indications of both theinstrumental and value rationality of the decision maker that enabledegrees of motivation to be derived. The indications of degrees ofintensity and likelihood that the decision maker is asked to provide areproducts of combinations of new external knowledge (e.g., currentevents), earlier learned internal knowledge (e.g., past experiences),and value judgments based on the values of the decision maker.

The fact that being asked to specify a degree of intensity leads toconsideration of how a possible outcome is perceived, and the fact thatbeing asked to specify a degree of likelihood in achieving or avoidingthat outcome leads to consideration of what the decision maker is ableand/or willing to do to achieve or avoid that outcome, also serves totrigger a form of what is sometimes referred to as “self regulation.”More precisely, by being asked to specify a degree of intensity, adecision maker is caused to evaluate how desirable or undesirable anoutcome is according to their values. And then, by being asked tospecify a degree of likelihood in achieving or avoiding that outcome,the decision maker is caused to consider whether the outcome can beachieved or avoided, what would be required to do so, and whether theyare willing to do what must be done (if anything can be done) to soachieve or avoid that outcome, thereby being caused to consider, inmultiple ways, what is realistically possible given their own abilities,in addition to what is more broadly possible based on othercircumstances not under their control.

FIG. 3H depicts an approach to implementing the relationship amongdegrees of intensity, likelihood and motivation within the system 1000in a simpler, two-dimensional tabular form that may be more efficientlyutilized by the processor(s) 550 or 750. Initially, the 1-7 scales fordegrees of intensity and likelihood are plotted along the axes, and thennumeric values in a range of 1-21 are used to fill the resulting 7×7table in a manner that functionally re-creates this relationship withinthe table. As also depicted in FIG. 3H, division of all 49 of thenumeric values within the 7×7 table may then be used to derive aproportionate set of percentage values within the 7×7 table. As will beexplained in greater detail, such proportionate percentage valuesarranged in such a table may then be used to implement the derivationfunction 544 d within the system 1000.

Referring back to FIGS. 3G and 3H, as depicted, the correlations ofnumeric values to scale texts for both intensity and likelihood do notinclude correlations for an intensity or likelihood of “never” or“zero”, respectively, or for “always” or “max”, respectively. This maycorrespond to a lack of inclusion of such scale texts to a decisionmaker as options to choose in specifying intensity or likelihood. Aswill be explained further, part of the purpose in eliciting input from adecision maker that is descriptive of degrees of both positive andnegative intensity and likelihood is to more methodically guide thedecision maker through considering both positive and negative aspects ofan outcome, or of a pair of possible successful and unsuccessfuloutcomes. Thus, if the option of specifying a degree of intensity of“zero” or a likelihood of “never” were made available to be selected bya decision maker, such a selection would provide a way by which thedecision maker could choose to effectively “opt out” of considering adegree of intensity or likelihood. By not providing such options to adecision maker, the decision maker is forced to grapple each option oreach outcome of each option having both positive and negative aspectsthat need to be considered.

FIGS. 3I-J, together, depict various aspects of the aggregation function544 a. Through further experimentation beyond the aforedescribedexperiments originally conducted by Mikhail Kotik, a relationship amongdegrees of positive motivation associated with a possible positiveoutcome of an option of a decision, negative motivation associated witha possible negative outcome of the same option, and an overallintegrated motivation was derived. FIG. 3I provides a graphical plot ofthis relationship among degrees of motivation for an option, where eachof the depicted non-linear gradations of “very high”, “high”, “middle”,“low” and “very low” describes a degree of the integrated motivation foran option. FIG. 3J depicts an embodiment of a 21×21 table derived fromsuch a graphical plot of a relationship among degrees of motivation(e.g., the graphical plot of FIG. 3I) in a manner similar to thederivation of the 7×7 tables of FIG. 3H from the graphical plot of FIG.3G. Like the 7×7 tables of FIG. 3H, the 21×21 table of FIG. 3J may bemore efficiently utilized by the processor(s) 550 or 750. Thus, and aswill shortly be explained, such a 21×21 table as is depicted in FIG. 3Hmay be used to implement the aggregation function 544 a with the system1000.

FIG. 3K depicts various aspects of an example of using the derivationfunction 544 d and the aggregation function 544 a in combination toderive an overall degree of motivation for one option being consideredin a current decision based on indications of degrees of intensity andlikelihood associated by a decision maker with that option. As depicted,the indications of degrees of intensity and likelihood are stored in adecision frame 371 c, which as discussed in reference to FIG. 2A, may bea version of decision frame 371 associated with operation of the system1000 in the compact mode. As a result, the decision maker may have beenprompted to provide a single set of indications of degrees of positiveintensity and likelihood, and of degrees of negative intensity andlikelihood. Indications of the selections of scale text made by thedecision maker to provide these four inputs may then be stored as thedepicted single IL frame 378 for the option.

As previously discussed in reference to FIGS. 3D and 3G, a decisionmaker may be guided through providing each indication of a degree ofintensity by being prompted to select a scale text from among aselection of scale texts that each specify a different degree ofintensity, and that together, define a range of degrees of intensity.Thus, in being guided through providing an indication of degree ofintensity, a decision maker may be prompted to select one of the scaletexts indicating a degree of intensity from a menu of scale texts thatmay include: “extremely weak”, “very weak”, “weak”, “not weak—notstrong”, “strong”, “very strong” and “extremely strong” (as earlierdepicted in FIG. 3G).

Similarly, the decision maker may be guided through providing eachindication of a degree of likelihood by being prompted to select a scaletext from among another selection of scale texts that each specify adifferent degree of likelihood, and that together, define a range ofdegrees of likelihood. Thus, in being guided through providing anindication of a degree of likelihood, a decision maker may be promptedto select a textual one of the scale texts indicating a degree oflikelihood from a menu of scale texts that may include: “extremelyseldom”, “very seldom”, “seldom”, “not seldom—no often”, “often”, “veryoften” and “extremely often” (as also earlier depicted in FIG. 3G).

As previously discussed in reference to FIG. 3G, the fact of the use ofselections of scale texts in the input of degrees of intensity andlikelihood to the system 1000 may necessitate first correlating eachsuch selection of a scale text to a corresponding numeric indication ofdegree. Again, in some embodiments, such correlations may be implementedas shown in FIG. 3G in which each of the available selections of scaletext is correlated to a numeric value on a numeric scale of 1 to 7. Asthose skilled in the art will readily recognize, at least in part due tothis approach of obtaining numeric indications of degree of likelihood(as well as degree of intensity) from a decision maker, the numericvalues for likelihood to which the selected scale texts are correlatedare non-probabilistic in nature.

With such correlations made, the numeric indications of degrees ofpositive intensity (+I) and positive likelihood (+L) from the single ILframe 378 may then be provided as inputs to one instance of thederivation function 544 d to derive a degree of positive motivation (+M)for the option. Similarly, the numeric indications of degrees ofnegative intensity (−I) and negative likelihood (−L) may then beprovided as inputs to another instance of the derivation function 544 dto derive a degree of negative motivation (−M) for the option. Aspreviously discussed in reference to FIG. 3H, the derivation function544 d may be implemented with a table not unlike the one depicted inFIG. 3H in which the degree of motivation that is derived therefrom isexpressed as a percentage.

With the degrees of positive motivation (+M) and negative motivation(−M) so derived for the depicted option, both of these degrees ofmotivation may be provided as inputs to an instance of the aggregationfunction 544 a to derive an overall degree of motivation for the option(OM). As previously discussed in reference to FIG. 3J, the aggregationfunction 544 a may be implemented with a table not unlike the onedepicted in FIG. 3J. In such embodiments, the percent value expressionsfor degree of positive motivation (+M) and for degree of negativemotivation (−M) generated by the two depicted instances of thederivation function 544 d may be plotted along corresponding ones of theaxes for positive and negative motivation to arrive at an aggregatedegree of motivation for the option (OM) that may also be expressed as apercentage.

Continuing with FIG. 3K, it should be noted that further experimentationhas shown that requiring a decision maker to provide indications ofdegrees of both positive and negative intensity, and to provideindications of degrees of both positive and negative likelihoods causesa decision maker to more fully and consistently consider both positiveand negative aspects of an option. Stated differently, imposing such arequirement results in the decision maker being caused to give separateconsideration to both of the positive and negative aspects, so thatconsideration of one does not overshadow and/or prevent consideration ofthe other. The eliciting of these separate degrees of intensity andlikelihood also enables their relative strengths to be taken intoaccount through there separate treatment and use in deriving the degreeof overall motivation (OM) for the option.

FIG. 3L depicts various aspects of another example of using thederivation function 544 d and the aggregation function 544 a incombination to derive an overall degree of motivation for one optionbeing considered in a current decision based on indications of degreesof intensity and likelihood associated by a decision maker with thatoption. As depicted, the indications of degrees of intensity andlikelihood are stored in a decision frame 371 e, which as discussed inreference to FIG. 2B, may be a version of decision frame 371 associatedwith operation of the system 1000 in the expanded mode. As a result, thedecision maker may have been prompted to provide two separate sets ofindications of degrees of positive intensity and likelihood, and ofdegrees of negative intensity and likelihood, with one set associatedwith a possible successful outcome of the option and the other setassociated with a possible unsuccessful outcome of the option. Each ofthese two sets of indications of degrees may then be stored in aseparate IL frame, specifically an IL frame 378 s associated with thepossible successful outcome, and an IL frame 378 u associated with thepossible unsuccessful outcome.

Again, a decision maker may be guided through providing each indicationof a degree of intensity by being prompted to select a scale text fromamong a selection of scale texts that each specify a different degree ofintensity, and that together, define a range of degrees of intensity.Similarly, the decision maker may be guided through providing eachindication of a degree of likelihood by being prompted to select a scaletext from among another selection of scale texts that each specify adifferent degree of likelihood, and that together, define a range ofdegrees of likelihood. Also again, the fact of the use of selections ofscale texts in the input of degrees of intensity and likelihood to thesystem 1000 may necessitate first correlating each such selection of ascale text to a corresponding numeric indication of degree.

With such correlations made, such numeric indications of positiveintensity (+I) and positive likelihood (+L) from the IL frame 378 s forthe possible successful outcome may then be provided as inputs to afirst instance of the derivation function 544 d to derive a degree ofpositive motivation (+M) for the possible successful outcome. Similarly,such numeric indications of negative intensity (−I) and negativelikelihood (−L) from the IL frame 378 s for the possible successfuloutcome may then be provided as inputs to a second instance of thederivation function 544 d to derive a degree of negative motivation (−M)for the possible successful outcome. Also similarly, such numericindications of positive intensity (+I) and positive likelihood (+L) fromthe IL frame 378 u for the possible unsuccessful outcome may then beprovided as inputs to a third instance of the derivation function 544 dto derive a degree of positive motivation (+M) for the possibleunsuccessful outcome. And also similarly, such numeric indications ofnegative intensity (−I) and negative likelihood (−L) from the IL frame378 u for the possible unsuccessful outcome may then be provided asinputs to a fourth instance of the derivation function 544 d to derive adegree of negative motivation (−M) for the possible unsuccessfuloutcome. Again, each of these instances of the derivation function 544 dmay be implemented with a table not unlike the one depicted in FIG. 3Hin which the degree of motivation that is derived therefrom is expressedas a percentage.

With the degrees of positive motivation (+M) so derived for both of thepossible successful and unsuccessful outcomes, these two degrees ofpositive motivation may be averaged together. Similarly, with thedegrees of negative motivation (−M) derived for both of the possiblesuccessful and unsuccessful outcomes, these two degrees of negativemotivation may also be averaged together. The two resulting averageddegrees of motivation, one positive and one negative, may then beprovided as inputs to a single instance of the aggregation function 544a to derive an overall degree of motivation for the option (OM). Again,the aggregation function 544 a may be implemented with a table notunlike the one depicted in FIG. 3J to arrive at an aggregate degree ofmotivation for the option (OM) that may also be expressed as apercentage.

Looking back upon FIGS. 3K and 3L, as has been discussed, and as will bepresented in greater detail in a more detailed example of use of thesystem 1000, a decision maker that has initially operated the system1000 in the compact mode may be prompted to switch to operating thesystem in the expanded mode as part of eliciting more detailed inputfrom the decision maker as part of causing further consideration of acurrent decision. As can be readily appreciated from a comparison ofthese two figures, such a transition from the compact mode to theexpanded mode, causes the eliciting of more detailed input concerningintensities and likelihood associated with possible successful andunsuccessful outcomes for each option such that the number of IL frames376 for each option is doubled. More specifically, and as depicted inFIG. 3L, still more input concerning degrees of intensity and likelihoodis elicited from the decision maker for each of the possible successfuloutcome and the possible unsuccessful outcome for each option. In otherwords, the decision maker is guided through considering each of thesetwo possible incomes separately to cause greater granularity ofconsideration for each, and accordingly, greater granularity in theinput concerning each that is provided to the system 1000. Thus, in theexpanded mode, the decision maker is caused to more fully consider boththe positive and negative aspects of each of these two possibleoutcomes.

Also in looking back upon FIGS. 3K and 3L, it should be noted that theeliciting of either one set of a quantity of four pieces of informationfrom a decision maker to fill a single IL frame 378, or two sets of aquantity of four pieces of information from a decision maker to file twoIL frames 378 s and 378 u, may not represent accidental choices of howmany pieces of information to request from a decision maker. Aspreviously discussed, the short-term memory of a human brain typicallyhas a capacity of four chunks of information. By requesting theprovision of four pieces of information from a decision maker inconsidering either an option in the compact mode, or in separatelyconsidering each of two possible outcomes of an option in the expandedmode, advantage is being taken of the availability of storage spacewithin the short-term memory for up to four chunks of information. Moreprecisely, with the earlier-described repetitive presentation ofdescriptive texts to aid in relieving the short-term memory from beingused to retain such information, the short-term memory is made morereadily available for use in retaining other information associated withthe current decision, including retaining up to four pieces ofinformation concerning positive and negative degrees of intensity andlikelihood corresponding to a single IL frame 378, 378 s or 378 u.

Also in looking back upon making such a transition from operation in thecompact mode (as exemplified in FIG. 3K) and operation in the expandedmode (as exemplified in FIG. 3L), the depicted addition of sub-branchesto a branch associated with a single option also illustrates a broaderfeature of the tree-like organization of information for a currentdecision in some embodiments of the system 1000. This broader featuremay the provision of a feature in the user interface that allows adecision maker to manually add further sub-branching for each of one ormore options as part of further decomposing aspects of the currentdecision to ever more granular parts to enable separate consideration tobe given to each of those more granular parts. By way of example,instead of transitioning from the compact mode to the expanded mode,thereby causing the automated addition of sub-branches to each of thebranches that are associated with a single option, it may be that thedecision maker finds just one of the options to be in need of furtherconsideration such that they view adding sub-branches thereto as usefulin further decomposing that option to better enable such furtherconsideration.

Also by way of example, it may be that a decision maker is alreadyoperating the system 1000 in the expanded mode such that there arealready sub-branches for each option that lead to separate IL frames 378s and 378 u to enable more granular consideration of each of thepossible successful and unsuccessful outcomes, respectively, for eachoption. However, it may be that, for one of the options, one or both ofthe possible successful and unsuccessful outcomes is proving to berather difficult to properly consider due to complexities of theadvantages and/or disadvantages of each. To better enable still morethorough (i.e., granular) consideration of such outcomes, it may be thatthe decision maker employs a feature in the user interface that enablesthe addition of sub-sub-branching to the sub-branch of one or both ofthe possible outcomes to enable separate consideration each advantageand/or disadvantage associated with either or both of the possibleoutcomes.

To assist a decision maker in making the most effective use of thesystem 1000, the user interface may additionally include at least theoptional presentation of explanatory texts (not to be confused witheither of the descriptive texts or the scale texts) that serve toexplain how best to understand the prompts to provide descriptive textsand/or the prompts to select scale texts indicative of degrees ofintensity and likelihood. By way of example, for the expanded mode,explanatory texts may at least optionally be presented that explain thenature the relationships among a longer term goal, a shorter term goaland the current decision. As part of such explanatory texts for expandedmode, it may also be explained that the descriptive texts that describeeach successful and unsuccessful outcome for each option should beassociated with success and lack thereof in meeting the shorter termgoal with which the current decision may be more closely associated,while in contrast, the scale texts indicating degrees of intensity andlikelihood should be selected to specify degrees of intensity andlikelihood, respectively, associated with success and lack thereof inmeeting the longer term goal. Such explanatory text may make it clearthat, in this way, both the longer term and shorter term goals are thenconsidered for the current decision. This is based on insights intoinstrumental rationality of the inventor, Alexander Yemelyanov, intousing instrumental rationality in decomposing a decision into smallerparts in a manner that leverages the shorter term goal and the longerterm goal. More specifically, in the expanded mode, each option shouldbe considered from the perspective of the shorter term goal, while thesuccessful and unsuccessful outcomes of an option should be consideredfrom the perspective of the longer term goal.

In some of such embodiments, at least some decision templates 331 mayprovide explanatory texts that may be at least optionally presented toprovide explanations concerning the selecting of scale texts for degreesof intensity and/or likelihood that may be more specific to theparticular type of decision that is being made. Where a decisiontemplate 331 is selected that provides such explanatory texts, it may bethat such explanatory texts provided by the decision text 331 isautomatically used in lieu of what may be more general explanatory textsthat are otherwise normally presented by the system 1000 to explainmaking such selections.

FIG. 3M depicts various aspects of using the overall degree of overallmotivation (OM) derived for each option of a current decision toidentify a best option from among multiple options of a currentdecision. As depicted, the comparator function 544 c may accept thedegree of overall motivation (OM) for each option as an input, and maysimply compare these degrees of motivation to identify which option hasthe highest degree of overall motivation (OM), which may then bedesignated as the best option. As depicted, an indication of such aresult may then be relayed to the interaction component 546 or 746, theexecution of which may cause processor(s) 550 or 750, respectively, togenerate a presentation of the results for being presented to a decisionmaker via the user interface provided by the operation of the display780 and input device 720.

As previously discussed, in some embodiments, the decision maker mayalso be presented with an indication of the degree of overall motivation(OM) associated with the best option. As will shortly be explained, suchan indication of degree of overall motivation (OM) may be accompanied byindications of the degrees of positive and negative motivation fromwhich the degree of overall motivation (OM) was generated.

Alternatively or additionally, and as also previously discussed, thedegrees of overall motivation (OM) for the multiple options of thecurrent decision may be analyzed to determine whether the degree ofoverall motivation (OM) for the best option at least meets a threshold,and/or to determine whether the degree of overall motivation (OM) forthe best option is higher than the degree of overall motivation (OM) foreach of the other options by at least another threshold. Depending onthe results of such analyses, the decision maker may also be presentedwith a prompt to further consider the current decision.

It should again be noted that, as an alternative to using the decisionmaking augmentation system 1000 in making a current decision, the system1000 may be used to review and evaluate a past decision that has alreadybeen made and acted upon. Again, by way of example, the system 1000 in apost-accident investigation to evaluate one or more decisions made by acrewmember of a jetliner, train, ship or other vehicle in response to anemergency situation that may have confronted that crewmember. In such asituation, there may have been no time or opportunity for any suchcrewmember to use any form of decision aid, etc., in making decisionsconcerning what action to take to address the emergency. However, afterthe emergency is over, it may be deemed valuable for purposes of anerror analysis and investigation (and for determining error-provokingsituations leading to recommendations) to use the system 1000 toevaluate each of the decisions made by such a crewmember to determinewhether changes should be made to training procedures, normal operatingprocedures, the design of the jetliner, ship, train, etc.

It should also be noted that the system 1000 may be used in both therole of making a decision and in the role of reviewing that decision ata later time. By way of example, the system 1000 by a doctor and apatient in a cooperative manner to arrive at a decision concerningmedical care to be provided by the doctor for the patient. As has beendiscussed, doing so may better enable a decision to be made that appliesboth instrumental rationality and value rationality based on the valuesof the patient. If, at a later time, a need should arise to evaluate thedecision that was made (e.g., the filing of a medical malpractice suitagainst the doctor), the decision frame 371 in which at least the inputsof descriptive texts and selections of scale text associated with thatdecision are stored may be retrieved and reviewed to evaluate aspects ofthe manner in which that decision was made.

It should be understood that, in situations in which the system 1000 isused in reviewing/evaluating a past decision in whatever context, theoperator of the system 1000 (i.e., the person who interacts with theprocessing device 700) may not be the decision maker who made that pastdecision. Instead, the operator of the system 1000 (again, the personwho interacts with the processing device 700) may be a supervisor of thedecision maker, an investigator of an incident with which the pastdecision is associated, or still another individual. Further, in suchreviewing/evaluating of a past decision, it should then be understoodthat so-called “current decision” for which inputs are provided to thesystem 1000 may actually be a re-creation or “re-performance” of thatpast decision.

FIGS. 4A through 4L, together, depict aspects of an example use of thedecision making augmentation functionality of the decision makingaugmentation system 1000 of either FIG. 1A or 1B in compact mode. FIGS.5A through 5E, together, depict aspects of an example use of thedecision making augmentation functionality of the decision makingaugmentation system 1000 of either FIG. 1A or 1B in expanded mode. Eachof FIGS. 4A-L and 5A-E depicts an example of the visual portion of auser interface presented to a decision maker on the display 780 of theprocessing device 700 with which the decision maker interacts. Nearlyall of these visual portions of the user interface include prompts toguide the decision maker through the provision of various inputsconcerning a current decision, including the descriptive texts and scaletexts, as has been discussed above.

Turning to FIG. 4A, as previously discussed in reference to FIG. 3B, incommencing the use of the system 1000 to assist in making a currentdecision, among the first prompts that may be presented to a decisionmaker on the display 780 of the processing device 700 may be a prompt tochoose between operating the system 1000 in the compact mode or in theexpanded mode. Also depicted is a prompt that may be presented in someembodiments to choose a decision template 331 for the current decision,as well as a prompt to specify the number of options to be considered inthe current decision.

As previously discussed, in some embodiments, access to the depictedtemplate database 330 in which numerous decision templates 331 may bestored may be restricted such that another prompt (not shown) may bepresented on the display 780 that requests the provision of securitycredentials. As also previously discussed, the selection of a particulardecision template 331 may dictate the number of options for the currentdecision.

As depicted, and regardless of whether a decision template 331 ischosen, the decision maker has selected the compact mode and a quantityof two options for the current decision.

Turning to FIG. 4B, as previously discussed in reference to FIG. 3C,with the compact mode having been selected, a prompt is presented on thedisplay 780 requesting entry of descriptive text that describes thecurrent decision. Also, with the number of options having been set, moreprompts are presented requesting entry of descriptive text thatdescribes each option of the current decision. However, as previouslydiscussed, if a decision template 331 was selected, then these depictedprompts may, instead, be prompts to edit default descriptive textprovided by the selected decision template 331 that provides defaultdescriptions of the current decision, and each of the options thereof.

Regardless of the exact origins of the descriptive text that describesthe current decision and each of its options, as previously discussed,in some embodiments, one or more of such descriptive texts may includeone or more references to decision aid(s) 311, such as the decision aids311 stored within the depicted aid database 310. Again, it may be thatdescriptive texts for each option within a decision template 331 includesuch references as part of an approach to providing the decision makerwith information concerning needed to better understand each option.

Turning to FIG. 4C, as previously discussed in reference to FIG. 3D,with the descriptive texts describing the current decision and eachoption having been provided by the decision maker, one or more of thosedescriptive texts may be repeatedly presented on the display 780throughout the time the decision maker interacts with the system 1000.As previously discussed, this may be part of an approach to obviatingthe need to use the limited storage capacity of the short-term memory ofthe decision maker's brain to retain the content of these descriptivetexts.

Also, with these descriptive texts having been provided by the decisionmaker, and now being repeatedly presented on the display 780, a promptmay also be presented on the display 780 that requests that the decisionmaker specify positive and negative degrees of intensity and likelihoodfor “Option 1” of the two options by selecting scale texts from each ofmultiple menus of selectable scale texts. As previously discussed,requiring the decision maker to specify a degree of intensity or outcomeby selecting a scale text from a set of scale texts serves to avoidhaving the brain of the decision maker slipping out of measuring andinto counting. As also previously discussed, the user interface mayadditionally provide at least the option of presenting explanatory textto provide an explanation to the decision maker of the meanings of eachof these degrees of intensity and likelihood.

As depicted, the decision maker has selected the scale texts “strong”,“often”, “very strong” and “often” for degrees of positive intensity,positive likelihood, negative intensity and negative likelihood,respectively. Indications of these selections of scale text may then bestored within a single IL frame 378 allocated for “Option 1.”

Turning to FIG. 4D, as previously discussed in reference to FIGS. 3Fthrough 3K, with indications of scale texts selected for “Option 1”having been stored within the single IL frame 378 allocated for “Option1”, those indications of such selections may first be correlated tonumeric values. Then, the ones of those numeric values indicative ofdegrees of positive intensity (+I) and positive likelihood (+L) may beprovided as inputs to a first instance of the derivation function 544 dto derive a degree of positive motivation (+M) for the option, and theones of those numeric values indicative of degrees of negative intensity(−I) and negative likelihood (−L) may be provided as inputs to a secondinstance of the derivation function 544 d to derive a degree of negativemotivation (−M) for the option. The resulting degrees of positivemotivation (+M) and negative motivation (−M) may then the be provided asinputs to an instance of the aggregation function 544 a to derive thedegree of overall motivation (OM) for the option.

With the degrees of overall motivation (OM), positive motivation (+M)and negative motivation (−M) having been derived for “Option 1”, thesedegrees of motivation for this option may be presented on the display780. As depicted, the degree of overall motivation (OM) for “Option 1”is 46%. As also depicted, the same descriptive texts as presented on thedisplay 780 in FIG. 4C, continue to be presented on the display 780 inFIG. 4D.

Turning to FIGS. 4E-F, in a manner very similar to what was justdiscussed in reference to FIGS. 4C-D, the decision maker may be promptedto provide degrees of positive and negative intensities and likelihoodfor “Option 2” through the selection of scale texts; degrees ofpositive, negative and overall motivation may be derived therefrom; andthose degrees of motivation may also be presented on the display 780.

With the degrees of overall motivation (OM), positive motivation (+M)and negative motivation (−M) having been derived for “Option 2”, thesedegrees of motivation for this option may be presented on the display780, alongside those for “Option 1.” As depicted, the degree of overallmotivation (OM) for “Option 2” is 31%. Again, as also depicted, the samedescriptive texts as presented on the display 780 in FIGS. 4C-D,continue to be presented on the display 780 in FIGS. 4E-F.

Turning to FIG. 4G, with at least the degrees of overall motivation (OM)having been derived for each of the two options, at least these twodegrees of overall motivation (OM) may then be provided as inputs to aninstance of the comparator function 544 c for comparison therebetween toidentify the best option from among these two options. As depicted, fromamong these two options, “Option 1” is identified as the best option,and an indication to that effect is presented on the display 780alongside an indication of at least the degree of overall motivation(OM) for “Option 1.”

As is also depicted, and as previously discussed in reference to FIG.3M, the degree of overall motivation (OM) for “Option 1” may be comparedto a threshold degree of overall motivation. If a degree of overallmotivation (OM) for an identified best option does not meet thatthreshold, then a warning indication that the overall motivation (OM)for that option is low along with a prompt for the decision maker tofurther consider the current decision. As depicted, the degree ofoverall motivation (OM) for “Option 1” is indicated to be low, andaccordingly, the decision maker is presented with prompts on the display780 to further consider the current decision by either returning to thecompact mode to edit its inputs, or switching to the expanded mode wherethe decision maker will be guided through providing more detailedinputs.

In some embodiments, it may be that a degree of overall motivation (OM)for an option that does not meet at a threshold of at least 50% isdeemed to be low. Other embodiments are possible in which a differentthreshold value may be used, possibly as a result of being establishedby experimentation.

Turning to FIG. 4H, in response to the indication of a low degree ofoverall motivation for “Option 1” in FIG. 4G, and in response to theprompts to further reconsider the current decision, the decision makerhas chosen to return to the compact mode to further consider the currentdecision. As also depicted, the decision maker has chosen to edit theinputs the inputs earlier provided in the compact mode in a manner thatincludes adding a third option.

It should be noted that, upon returning to the compact mode, thedecision maker may be able to use the user interface to edit the inputsthereof in any of a variety of ways, either in lieu of or in addition toadding a third option. By way of example, the decision maker may chooseto alter selections of scale text for either of “Option 1” or “Option 2”as part of experimenting with such changes to try an understand howslight variations in those inputs may or may not affect theidentification of a best option and/or the degree of overall motivation(OM) of the identified best option. By way of another example, thedecision maker may choose to alter the descriptive text that describesthe current decision and/or the descriptive text for one or both of theoriginal two options as part of re-framing the current decision.

It should also be noted that the decision maker could have also returnedto the compact mode to make any or all of such changes to the inputsthereto, even if a best option had been identified and presented on thedisplay 780 without such a warning or caveat as the degree of overallmotivation (OM) being low.

Turning to FIG. 4I, in a manner very similar to what was discussed inreference to FIG. 4B, with the compact mode continuing to be selected,and with the number of options now increased from two to three, a promptis presented on the display 780 requesting entry of descriptive textthat describes the third option.

Turning to FIGS. 4J-K, in a manner very similar to what was discussed inreference to FIGS. 4C-D, and in reference to FIGS. 4E-F, the decisionmaker may be prompted to provide degrees of positive and negativeintensities and likelihood for “Option 3” through the selection of scaletexts; degrees of positive, negative and overall motivation may bederived therefrom; and those degrees of motivation may also be presentedon the display 780.

With the degrees of overall motivation (OM), positive motivation (+M)and negative motivation (−M) having been derived for “Option 3”, thesedegrees of motivation for this option may be presented on the display780, alongside those for “Option 1” and “Option 2.” As depicted, thedegree of overall motivation (OM) for “Option 3” is 49%. Again, as alsodepicted, the same descriptive texts as presented on the display 780 inFIGS. 4C-D, and in 4E-F, continue to be presented on the display 780 inFIGS. 4J-K, but now accompanied by the descriptive text that describes“Option 3.”

Turning to FIG. 4L, in a manner very similar to what was discussed inreference to FIG. 4G, with at least the degrees of overall motivation(OM) having now been derived for all three options, at least these threedegrees of overall motivation (OM) may then be provided as inputs to aninstance of the comparator function 544 c for comparison thereamong toidentify the best option from among these three options. As depicted,from among these three options, “Option 3” is now identified as the bestoption, and an indication to that effect is presented on the display 780alongside an indication of at least the degree of overall motivation(OM) for “Option 3.”

As is also depicted, the degree of overall motivation (OM) for “Option3” may be compared to the same threshold degree of overall motivation asthe degree of overall motivation (OM) for “Option 1” was in FIG. 4G. Asdepicted, the degree of overall motivation (OM) for “Option 3” is nowindicated to be low, and accordingly, the decision maker is againpresented with prompts on the display 780 to further consider thecurrent decision by either returning to the compact mode to edit itsinputs, or switching to the expanded mode where the decision maker willbe guided through providing more detailed inputs.

Turning to FIG. 5A, in response to the indication of a low degree ofoverall motivation for “Option 3” in FIG. 4L, and in response to theprompts to further reconsider the current decision, the decision makerhas now chosen to switch to the expanded mode to further consider thecurrent decision. As also depicted, the decision maker is now presentedwith a greater variety of prompts to provide (or edit) a greater overallquantity of descriptive texts.

More specifically, and in a manner very similar to what was discussed inreference to FIG. 4B, with the expanded mode having been selected, aprompt is presented on the display 780 requesting entry (or editing) ofa descriptive text that describes the current decision, but nowaccompanied by addition prompts requesting entry of additionaldescriptive texts that describe an associated longer term goal and anassociated shorter term goal. Also, with the number of options havingbeen set, more prompts are presented requesting entry (or editing) ofdescriptive texts that describe each option of the current decision, butnow accompanied by additional prompts requesting entry of additionaldescriptive texts that describe successful and unsuccessful outcomes foreach option.

Turning to FIG. 5B, in a manner similar to what was discussed inreference to FIGS. 4C, 4E and 4J, with the descriptive texts describingthe current decision, shorter and longer term goals, each option andeach outcome of each option having been provided by the decision maker,one or more of those descriptive texts may be repeatedly presented onthe display 780 throughout the time the decision maker interacts withthe system 1000. Again, this may be part of an approach to obviating theneed to use the limited storage capacity of the short-term memory of thedecision maker's brain to retain the content of these descriptive texts.

Also, with these descriptive texts having been provided by the decisionmaker, and now being repeatedly presented on the display 780, a promptmay also be presented on the display 780 that requests that the decisionmaker specify positive and negative degrees of intensity and likelihoodfor the possible successful outcome of “Option 1” of the two options byselecting scale texts from each of multiple menus of selectable scaletexts. Again, requiring the decision maker to specify a degree ofintensity or outcome by selecting a scale text from a set of scale textsserves to avoid having the brain of the decision maker slipping out ofmeasuring and into counting. And again, the user interface mayadditionally provide at least the option of presenting explanatory textto provide an explanation to the decision maker of the meanings of eachof these degrees of intensity and likelihood.

As depicted, the decision maker has selected the scale texts “verystrong”, “seldom”, “not weak, not strong” and “not seldom, not often”for degrees of positive intensity, positive likelihood, negativeintensity and negative likelihood, respectively. Indications of theseselections of scale text may then be stored within the IL frame 378 sallocated for the possible successful outcome of “Option 1.”

Turning to FIG. 5C, in a manner very similar to what was just discussedin reference to FIG. 5C, a prompt may also be presented on the display780 that requests that the decision maker specify positive and negativedegrees of intensity and likelihood for the possible unsuccessfuloutcome of “Option 1” of the two options, again by selecting scale textsfrom each of multiple menus of selectable scale texts.

As depicted, the decision maker has selected the scale texts “weak”,“often”, “very strong” and “very seldom” for degrees of positiveintensity, positive likelihood, negative intensity and negativelikelihood, respectively. Indications of these selections of scale textmay then be stored within the IL frame 378 u allocated for the possibleunsuccessful outcome of “Option 1.”

As also depicted, the same descriptive texts as presented on the display780 in FIG. 5B, continue to be presented on the display 780 in FIG. 5C.

Moving on from FIG. 5C, similar prompts for the entry of degrees ofintensity and likelihood for both of the possible successful andunsuccessful outcomes of “Option 2.” However, for the sake of brevity byavoiding unnecessary repetitive clutter in these figures, specificfigures depicting these operations are not included herein.

Turning to FIG. 5D, in a manner similar to what was discussed inreference to FIGS. 4D, 4F and 4K, with indications of scale textsselected for the possible successful and unsuccessful outcomes of“Option 2” having been stored within their respective IL frames 378 sand 378 u, those indications of such selections may first be correlatedto numeric values.

Then, the ones of those numeric values indicative of degrees of positiveintensity (+I) and positive likelihood (+L) for the possible successfuloutcome may be provided as inputs to a first instance of the derivationfunction 544 d to derive a degree of positive motivation (+M) for thepossible successful outcome, and the ones of those numeric valuesindicative of degrees of negative intensity (−I) and negative likelihood(−L) for the possible successful outcome may be provided as inputs to asecond instance of the derivation function 544 d to derive a degree ofnegative motivation (−M) for the possible successful outcome.Correspondingly, the ones of those numeric values indicative of degreesof positive intensity (+I) and positive likelihood (+L) for the possibleunsuccessful outcome may be provided as inputs to a third instance ofthe derivation function 544 d to derive a degree of positive motivation(+M) for the possible unsuccessful outcome, and the ones of thosenumeric values indicative of degrees of negative intensity (−I) andnegative likelihood (−L) for the possible unsuccessful outcome may beprovided as inputs to a fourth instance of the derivation function 544 dto derive a degree of negative motivation (−M) for the possibleunsuccessful outcome.

The resulting degrees of positive motivation (+M) for both of thepossible successful and unsuccessful outcomes may then be averagedtogether before being provided as one of two inputs to an instance ofthe aggregation function 544 a. Correspondingly, resulting degrees ofnegative motivation (−M) for both of the possible successful andunsuccessful outcomes may then be averaged together before beingprovided as the other of the two inputs to the instance of theaggregation function 544 a. With these two inputs so provided, theinstance of the aggregation function 544 a may then be used to derivethe degree of overall motivation (OM) for the option.

With the degrees of overall motivation (OM), positive motivation (+M)and negative motivation (−M) having been derived for “Option 2”, thesedegrees of motivation for this option may be presented on the display780. As depicted, the degree of overall motivation (OM) for “Option 2”is 53%. As also depicted, the same descriptive texts as presented on thedisplay 780 in FIGS. 5B-C, continue to be presented on the display 780in FIG. 5D.

It should be noted that, for sake of brevity by avoiding unnecessaryrepetitive clutter in these figures, a specific figure depicting theseoperations also being performed to derive degrees of motivation for“Option 1” is not included herein. However, FIG. 5D does depict theresults of such operations having been performed for “Option 1.”Specifically, FIG. 5D additionally depicts the presentation of thedegrees of motivation for “Option 1” on the display 780, with the degreeof overall motivation (OM) for “Option 1” being 51%.

Turning to FIG. 5E, in a manner similar to what was discussed inreference to FIGS. 4G and 4L, with at least the degrees of overallmotivation (OM) having been derived for each of the two options, atleast these two degrees of overall motivation (OM) may then be providedas inputs to an instance of the comparator function 544 c for comparisontherebetween to identify the best option from among these two options.As depicted, from among these two options, “Option 2” is identified asthe best option, and an indication to that effect is presented on thedisplay 780 alongside an indication of at least the degree of overallmotivation (OM) for “Option 2.”

As is also depicted, and as also discussed in reference to FIG. 3M, thedegree of overall motivation (OM) for “Option 2” may be compared to athreshold degree of overall motivation. Again, if a degree of overallmotivation (OM) for an identified best option does not meet thatthreshold, then a warning indication that the overall motivation (OM)for that option is low along with a prompt for the decision maker tofurther consider the current decision. However, as depicted, the degreeof overall motivation (OM) for “Option 2” has at least met thatthreshold as there is no indication provided that the degree of overallmotivation (OM) for “Option 2” is low.

However, as discussed in reference to FIG. 3M, the degree of overallmotivation (OM) for “Option 2” may be compared to the degree of overallmotivation (OM) for “Option 1.” If a degree of overall motivation (OM)for an identified best option does not exceed the degree of overallmotivation (OM) of every other option by at least a pre-determinedthreshold difference in degree, then a proximity warning indication thatthe degree of overall motivation (OM) for that option is notsufficiently higher than the degree of overall motivation (OM) for atleast one other option. Such an indication may also be accompanied by aprompt for the decision maker to further consider the current decision.As depicted, the decision maker is presented with a prompt on thedisplay 780 to further consider the current decision by returning to theexpanded mode to edit its inputs.

There is thus disclosed a decision making augmentation system of one ormore devices that implements a method for both augmenting the short-termmemory of the brain of a decision maker, and guiding the decision makerthrough considering positive and negative aspects (“pros and cons”) ofeach option of a current decision in a manner that integrates bothinstrumental rationality and value rationality based on the values ofthe decision maker. The features set forth below may be combined in anyof a variety of ways to create any of a variety of embodiments of such asystem and/or of a method of decision making augmentation that mayincorporate such a system.

A decision making augmentation system includes: a manual input deviceconfigured to enable entry of text input by an operator of the decisionmaking augmentation system that describes aspects of a current decisionincluding a selection of one option from among multiple options; adisplay configured to visually guide the operator through providing thetext input; a storage configured to store indications of the text input,wherein the text input includes at least one of multiple descriptivetexts and multiple selections of scale text; and a processorcommunicatively coupled to at least the storage. The processor isconfigured to perform operations including: receive a decisiondescriptive text of the multiple descriptive texts, wherein the decisiondescriptive text describes the current decision; and cause repeatedpresentation of the decision descriptive text on the display. Theprocessor is also configured to, for each option of the multipleoptions, perform operations including: receive an indication of at leastone selection of scale text of the multiple selections of scale text,wherein the at least one selection of scale text specifies either adegree of intensity of seeking to achieve or avoid a possible outcome ofthe option, or a degree of likelihood of achieving or avoiding thepossible outcome of the option; and derive a degree of overallmotivation associated with the option based on the at least oneselection of scale text. The processor is further configured to:identify a best option from among the multiple options based on thedegree of overall motivation associated with each option; cause apresentation of an indication of the best option on the display; andcompare the degree of overall motivation associated with the best optionto a threshold degree of overall motivation. The processor is stillfurther configured to, in response to the degree of overall motivationassociated with the best option being less than the threshold degree ofoverall motivation, cause a presentation, on the display, of: a warningthat the degree of overall motivation associated with the best option islow; and a prompt for the operator to further consider the currentdecision. The processor is yet further configured to compare the degreeof overall motivation associated with the best option to the degree ofoverall motivation associated with each other option of the multipleoptions. The processor is also yet further configured to, in response tothe degree of overall motivation associated with the best option notexceeding, by at least a threshold degree of difference in overallmotivation, the degree of overall motivation associated with at leastone other option of the multiple options, cause a presentation, on thedisplay of: a proximity warning that the difference in degree of theoverall motivation associated with the best option from the overallmotivation associated with at least one other option is low; and theprompt for the operator to further consider the current decision.

The processor may be further configured to cause a presentation, on thedisplay, of a prompt for the operator to enter at least one of: thedecision descriptive text; a longer term goal descriptive text of themultiple descriptive texts that describes a longer term goal associatedwith the current decision; and a shorter term goal descriptive text ofthe multiple descriptive texts that describes a shorter term goalassociated with the current decision. The processor may also be furtherconfigured to, during each presentation, on the display, of a prompt forthe operator to select scale text that specifies either a degree ofintensity or a degree of likelihood associated with an option of themultiple options, cause a presentation, on the display, of at least oneof the decision descriptive text, the longer term goal descriptive text,and the shorter term goal descriptive text.

The processor may be further configured to cause a presentation, on thedisplay, of at least one prompt for the operator to enter, for eachoption of the multiple options, at least one of: a first optiondescriptive text of the multiple descriptive texts that describes theoption; a second option descriptive text of the multiple descriptivetexts that describes a possible successful outcome of the option; and athird option descriptive text of the multiple descriptive texts thatdescribes a possible unsuccessful outcome of the option. The processormay also be further configured to, during each presentation, on thedisplay, of a prompt for the operator to select scale text thatspecifies either a degree of intensity or a degree of likelihoodassociated with an option of the multiple options, cause a presentation,on the display, of at least one of the first option descriptive text,the longer term goal descriptive text, and the shorter term goaldescriptive text.

Receiving the decision descriptive text may include receiving anindication of selection of a decision template by the operator, theselected decision template may include a default description of thecurrent decision, and the processor may be further configured to: acceptthe default description of the current decision from the selecteddecision template as the decision descriptive text; and cause apresentation, on the display, of a prompt for the operator to edit thedecision descriptive text.

The selected decision template may specify a quantity of the multipleoptions. The selected decision template, for each option of the multipleoptions, may include at least one of: a first option descriptive text ofthe multiple descriptive texts that describes the option; a secondoption descriptive text of the multiple descriptive texts that describesa possible successful outcome of the option; and a third optiondescriptive text of the multiple descriptive texts that describes apossible unsuccessful outcome of the option. At least one of thedecision descriptive text, and the first option descriptive textassociated with at least one option of the multiple options, may includea reference to a decision aid to provide the operator within informationconcerning a subject associated with the current decision. The processormay be further configured to cause a presentation, on the display andfor each option, a prompt for the operator to edit at least one of thefirst option descriptive text, the second option descriptive text andthe third option descriptive text.

For each option, receiving an indication of at least one selection ofscale text may include: receiving an indication of a selection of scaletext specifying a degree of positive intensity of seeking to achieve apossible positive outcome of the option; receiving an indication of aselection of scale text specifying a degree of positive likelihood ofachieving the possible positive outcome; receiving an indication of aselection of scale text specifying a degree of negative intensity ofseeking to avoid a possible negative outcome of the option; andreceiving an indication of a selection of scale text specifying a degreeof negative likelihood of avoiding the possible negative outcome. Also,for each option, deriving a degree of overall motivation associated withthe option may include: deriving a degree of positive motivationassociated with the option based on the degree of positive intensity andthe degree of positive likelihood; deriving a degree of negativemotivation associated with the option based on the degree of negativeintensity and the degree of negative likelihood; and deriving the degreeof overall motivation associated with the option based on the degree ofpositive motivation associated with the option and the degree ofnegative motivation associated with the option.

For each option, receiving an indication of at least one selection ofscale text may include: receiving an indication of a selection of scaletext specifying a first degree of positive intensity of seeking toachieve a possible successful outcome of the option; receiving anindication of a selection of scale text specifying a first degree ofpositive likelihood of achieving the possible successful outcome;receiving an indication of a selection of scale text specifying a firstdegree of negative intensity of seeking to avoid the possible successfuloutcome of the option; receiving an indication of a selection of scaletext specifying a first degree of negative likelihood of avoiding thepossible successful outcome; receiving an indication of a selection ofscale text specifying a second degree of positive intensity of seekingto achieve a possible unsuccessful outcome of the option; receiving anindication of a selection of scale text specifying a second degree ofpositive likelihood of achieving the possible unsuccessful outcome;receiving an indication of a selection of scale text specifying a seconddegree of negative intensity of seeking to avoid the possibleunsuccessful outcome of the option; and receiving an indication of aselection of scale text specifying a second degree of negativelikelihood of avoiding the possible unsuccessful outcome. Also, for eachoption, deriving a degree of overall motivation associated with theoption may include: deriving a first degree of positive motivationassociated with the possible successful outcome based on the firstdegree of positive intensity and the first degree of positivelikelihood; deriving a second degree of positive motivation associatedwith the possible unsuccessful outcome based on the second degree ofpositive intensity and the second degree of positive likelihood;deriving a first degree of negative motivation associated with thepossible successful outcome based on the first degree of negativeintensity and the first degree of negative likelihood; deriving a seconddegree of negative motivation associated with the possible unsuccessfuloutcome based on the second degree of negative intensity and the seconddegree of negative likelihood; and deriving the degree of overallmotivation associated with the option based on an average of the firstdegree of positive motivation and the second degree of positivemotivation, and an average of the first degree of negative motivationand the second degree of negative motivation.

Identifying the best option from among the multiple options may includeselecting the option associated with highest degree of overallmotivation among the multiple options.

The decision making augmentation system may be operable in either acompact mode or an expanded mode, and the decision making augmentationsystem may be initially operated in the compact mode, and the prompt forthe operator to further consider the current decision may include aprompt for the operator to switch to operating the decision makingaugmentation system in the expanded mode. The processor may beconfigured to perform operations including, in the compact mode, foreach option, cause a presentation, on the display, of at least oneprompt for the operator to select scale texts specifying: a degree ofpositive intensity of seeking to achieve a possible positive outcome ofthe option; a degree of positive likelihood of achieving the possiblepositive outcome; a degree of negative intensity of seeking to avoid apossible negative outcome of the option; and a degree of negativelikelihood of avoiding the possible negative outcome. The processor mayalso be configured to perform operations including, in the expandedmode, for each option, cause a presentation, on the display, of at leastone prompt for the operator to select scale texts specifying: a firstdegree of positive intensity of seeking to achieve a possible successfuloutcome of the option; a first degree of positive likelihood ofachieving the possible successful outcome; a first degree of negativeintensity of seeking to avoid the possible successful outcome of theoption; a first degree of negative likelihood of avoiding the possiblesuccessful outcome; a second degree of positive intensity of seeking toachieve a possible unsuccessful outcome of the option; a second degreeof positive likelihood of achieving the possible unsuccessful outcome; asecond degree of negative intensity of seeking to avoid the possibleunsuccessful outcome of the option; and a second degree of negativelikelihood of avoiding the possible unsuccessful outcome.

At least a portion of the decision making augmentation system may beincorporated into a control system of a vehicle configured to carry atleast one of passengers and cargo; and the processor may be configuredto cause a presentation, on the display, of a prompt to select adecision template that specifies aspects of a decision that closelyresembles the current decision.

A method of decision making augmentation includes: receiving, at aprocessor of a decision making augmentation system, and via a manualinput device configured to enable entry of text input by an operator, adecision descriptive text of multiple descriptive texts, wherein thedecision descriptive text describes a current decision including aselection of one option from among multiple options; and causingrepeated presentation of the decision descriptive text on a displayconfigured to visually guide the operator through providing the textinput, wherein the text input includes at least one of the multipledescriptive texts and multiple selections of scale text. The method alsoincludes, for each option of the multiple options, performing operationsincluding: receiving, at the processor, and via the manual input device,an indication of at least one selection of scale text of the multipleselections of scale text, wherein the at least one selection of scaletext specifies either a degree of intensity of seeking to achieve oravoid a possible outcome of the option, or a degree of likelihood ofachieving or avoiding the possible outcome of the option; and deriving,by the processor, a degree of overall motivation associated with theoption based on the at least one selection of scale text. The methodfurther includes: identifying, by the processor, a best option fromamong the multiple options based on the degree of overall motivationassociated with each option; causing a presentation of an indication ofthe best option on the display; and comparing, by the processor, thedegree of overall motivation associated with the best option to athreshold degree of overall motivation. The method still furtherincludes, in response to the degree of overall motivation associatedwith the best option being less than the threshold degree of overallmotivation, causing a presentation, on the display, of: a warning thatthe degree of overall motivation associated with the best option is low;and a prompt for the operator to further consider the current decision.The method yet further includes comparing, by the processor, the degreeof overall motivation associated with the best option to the degree ofoverall motivation associated with each other option of the multipleoptions. The method also yet further includes, in response to the degreeof overall motivation associated with the best option not exceeding, byat least a threshold degree of difference in overall motivation, thedegree of overall motivation associated with at least one other optionof the multiple options, causing a presentation, on the display of: aproximity warning that the difference in degree of the overallmotivation associated with the best option from the overall motivationassociated with at least one other option is low; and the prompt for theoperator to further consider the current decision.

The method may further include causing a presentation, on the display,of a prompt for the operator to enter at least one of: the decisiondescriptive text; a longer term goal descriptive text of the multipledescriptive texts that describes a longer term goal associated with thecurrent decision; and a shorter term goal descriptive text of themultiple descriptive texts that describes a shorter term goal associatedwith the current decision. The method may also further include, duringeach presentation, on the display, of a prompt for the operator toselect scale text that specifies either a degree of intensity or adegree of likelihood associated with an option of the multiple options,causing a presentation, on the display, of at least one of the decisiondescriptive text, the longer term goal descriptive text, and the shorterterm goal descriptive text.

The method may further include causing a presentation, on the display,of at least one prompt for the operator to enter, for each option of themultiple options, at least one of: a first option descriptive text ofthe multiple descriptive texts that describes the option; a secondoption descriptive text of the multiple descriptive texts that describesa possible successful outcome of the option; and a third optiondescriptive text of the multiple descriptive texts that describes apossible unsuccessful outcome of the option. The method may also furtherinclude, during each presentation, on the display, of a prompt for theoperator to select scale text that specifies either a degree ofintensity or a degree of likelihood associated with an option of themultiple options, causing a presentation, on the display, of at leastone of the first option descriptive text, the longer term goaldescriptive text, and the shorter term goal descriptive text.

Receiving the decision descriptive text may include receiving anindication of selection of a decision template by the operator, theselected decision template may include a default description of thecurrent decision, and the method may further include: accepting thedefault description of the current decision from the selected decisiontemplate as the decision descriptive text; and causing a presentation,on the display, of a prompt for the operator to edit the decisiondescriptive text.

The selected decision template may specify a quantity of the multipleoptions. The selected decision template, for each option of the multipleoptions, may include at least one of: a first option descriptive text ofthe multiple descriptive texts that describes the option; a secondoption descriptive text of the multiple descriptive texts that describesa possible successful outcome of the option; and a third optiondescriptive text of the multiple descriptive texts that describes apossible unsuccessful outcome of the option. At least one of thedecision descriptive text, and the first option descriptive textassociated with at least one option of the multiple options, may includea reference to a decision aid to provide the operator within informationconcerning a subject associated with the current decision. The methodmay further include causing a presentation, on the display and for eachoption, a prompt for the operator to edit at least one of the firstoption descriptive text, the second option descriptive text and thethird option descriptive text.

For each option, receiving an indication of at least one selection ofscale text may include: receiving an indication of a selection of scaletext specifying a degree of positive intensity of seeking to achieve apossible positive outcome of the option; receiving an indication of aselection of scale text specifying a degree of positive likelihood ofachieving the possible positive outcome; receiving an indication of aselection of scale text specifying a degree of negative intensity ofseeking to avoid a possible negative outcome of the option; andreceiving an indication of a selection of scale text specifying a degreeof negative likelihood of avoiding the possible negative outcome. Also,for each option, deriving a degree of overall motivation associated withthe option may include: deriving a degree of positive motivationassociated with the option based on the degree of positive intensity andthe degree of positive likelihood; deriving a degree of negativemotivation associated with the option based on the degree of negativeintensity and the degree of negative likelihood; and deriving the degreeof overall motivation associated with the option based on the degree ofpositive motivation associated with the option and the degree ofnegative motivation associated with the option.

For each option, receiving an indication of at least one selection ofscale text may include: receiving an indication of a selection of scaletext specifying a first degree of positive intensity of seeking toachieve a possible successful outcome of the option; receiving anindication of a selection of scale text specifying a first degree ofpositive likelihood of achieving the possible successful outcome;receiving an indication of a selection of scale text specifying a firstdegree of negative intensity of seeking to avoid the possible successfuloutcome of the option; receiving an indication of a selection of scaletext specifying a first degree of negative likelihood of avoiding thepossible successful outcome; receiving an indication of a selection ofscale text specifying a second degree of positive intensity of seekingto achieve a possible unsuccessful outcome of the option; receiving anindication of a selection of scale text specifying a second degree ofpositive likelihood of achieving the possible unsuccessful outcome;receiving an indication of a selection of scale text specifying a seconddegree of negative intensity of seeking to avoid the possibleunsuccessful outcome of the option; and receiving an indication of aselection of scale text specifying a second degree of negativelikelihood of avoiding the possible unsuccessful outcome. Also, for eachoption, deriving a degree of overall motivation associated with theoption may include: deriving a first degree of positive motivationassociated with the possible successful outcome based on the firstdegree of positive intensity and the first degree of positivelikelihood; deriving a second degree of positive motivation associatedwith the possible unsuccessful outcome based on the second degree ofpositive intensity and the second degree of positive likelihood;deriving a first degree of negative motivation associated with thepossible successful outcome based on the first degree of negativeintensity and the first degree of negative likelihood; deriving a seconddegree of negative motivation associated with the possible unsuccessfuloutcome based on the second degree of negative intensity and the seconddegree of negative likelihood; and deriving the degree of overallmotivation associated with the option based on an average of the firstdegree of positive motivation and the second degree of positivemotivation, and an average of the first degree of negative motivationand the second degree of negative motivation.

Identifying the best option from among the multiple options may includeselecting the option associated with highest degree of overallmotivation among the multiple options.

The decision making augmentation system may be operable in either acompact mode or an expanded mode, and the decision making augmentationsystem may be initially operated in the compact mode, and the prompt forthe operator to further consider the current decision may include aprompt for the operator to switch to operating the decision makingaugmentation system in the expanded mode. The method may furtherinclude, in the compact mode, for each option, causing a presentation,on the display, of at least one prompt for the operator to select scaletexts specifying: a degree of positive intensity of seeking to achieve apossible positive outcome of the option; a degree of positive likelihoodof achieving the possible positive outcome; a degree of negativeintensity of seeking to avoid a possible negative outcome of the option;and a degree of negative likelihood of avoiding the possible negativeoutcome. The method may also further include, in the expanded mode, foreach option, causing a presentation, on the display, of at least oneprompt for the operator to select scale texts specifying: a first degreeof positive intensity of seeking to achieve a possible successfuloutcome of the option; a first degree of positive likelihood ofachieving the possible successful outcome; a first degree of negativeintensity of seeking to avoid the possible successful outcome of theoption; a first degree of negative likelihood of avoiding the possiblesuccessful outcome; a second degree of positive intensity of seeking toachieve a possible unsuccessful outcome of the option; a second degreeof positive likelihood of achieving the possible unsuccessful outcome; asecond degree of negative intensity of seeking to avoid the possibleunsuccessful outcome of the option; and a second degree of negativelikelihood of avoiding the possible unsuccessful outcome.

At least a portion of the decision making augmentation system may beincorporated into a control system of a vehicle configured to carry atleast one of passengers and cargo; and the method may further includecausing a presentation, on the display, of a prompt to select a decisiontemplate that specifies aspects of a decision that closely resembles thecurrent decision.

Various other components may be included and called upon for providingfor aspects of the teachings herein. For example, additional materials,combinations of materials, and/or omission of materials may be used toprovide for added embodiments that are within the scope of the teachingsherein.

Standards for performance, selection of materials, functionality, andother discretionary aspects are to be determined by a user, designer,manufacturer, or other similarly interested party. Any standardsexpressed herein are merely illustrative and are not limiting of theteachings herein.

When introducing elements of the present invention or the embodiment(s)thereof, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. Similarly, the adjective“another,” when used to introduce an element, is intended to mean one ormore elements. The terms “including” and “having” are intended to beinclusive such that there may be additional elements other than thelisted elements.

While the invention has been described with reference to illustrativeembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications will be appreciated by those skilled in theart to adapt a particular instrument, situation or material to theteachings of the invention without departing from the essential scopethereof. Therefore, it is intended that the invention not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this invention, but that the invention will include allembodiments falling within the scope of the appended claims.

1. A decision making augmentation system comprising: a manual inputdevice configured to enable entry of text input by an operator of thedecision making augmentation system that describes aspects of a currentdecision comprising a selection of one option from among multipleoptions; a display configured to visually guide the operator throughproviding the text input; a storage configured to store indications ofthe text input, wherein the text input comprises at least one ofmultiple descriptive texts and multiple selections of scale text; and aprocessor communicatively coupled to at least the storage, the processorconfigured to perform operations comprising: receive a decisiondescriptive text of the multiple descriptive texts, wherein the decisiondescriptive text describes the current decision; cause repeatedpresentation of the decision descriptive text on the display; for eachoption of the multiple options, perform operations comprising: receivean indication of at least one selection of scale text of the multipleselections of scale text, wherein the at least one selection of scaletext specifies either a degree of intensity of seeking to achieve oravoid a possible outcome of the option, or a degree of likelihood ofachieving or avoiding the possible outcome of the option; and derive adegree of overall motivation associated with the option based on the atleast one selection of scale text; identify a best option from among themultiple options based on the degree of overall motivation associatedwith each option; cause a presentation of an indication of the bestoption on the display; compare the degree of overall motivationassociated with the best option to a threshold degree of overallmotivation; in response to the degree of overall motivation associatedwith the best option being less than the threshold degree of overallmotivation, cause a presentation, on the display, of: a warning that thedegree of overall motivation associated with the best option is low; anda prompt for the operator to further consider the current decision;compare the degree of overall motivation associated with the best optionto the degree of overall motivation associated with each other option ofthe multiple options; and in response to the degree of overallmotivation associated with the best option not exceeding, by at least athreshold degree of difference in overall motivation, the degree ofoverall motivation associated with at least one other option of themultiple options, cause a presentation, on the display of: a proximitywarning that the difference in degree of the overall motivationassociated with the best option from the overall motivation associatedwith at least one other option is low; and the prompt for the operatorto further consider the current decision.
 2. The decision makingaugmentation system of claim 1, wherein the processor is furtherconfigured to: cause a presentation, on the display, of a prompt for theoperator to enter at least one of: the decision descriptive text; alonger term goal descriptive text of the multiple descriptive texts thatdescribes a longer term goal associated with the current decision; and ashorter term goal descriptive text of the multiple descriptive textsthat describes a shorter term goal associated with the current decision;and during each presentation, on the display, of a prompt for theoperator to select scale text that specifies either a degree ofintensity or a degree of likelihood associated with an option of themultiple options, cause a presentation, on the display, of at least oneof the decision descriptive text, the longer term goal descriptive text,and the shorter term goal descriptive text.
 3. The decision makingaugmentation system of claim 2, wherein the processor is furtherconfigured to: cause a presentation, on the display, of at least oneprompt for the operator to enter, for each option of the multipleoptions, at least one of: a first option descriptive text of themultiple descriptive texts that describes the option; a second optiondescriptive text of the multiple descriptive texts that describes apossible successful outcome of the option; and a third optiondescriptive text of the multiple descriptive texts that describes apossible unsuccessful outcome of the option; during each presentation,on the display, of a prompt for the operator to select scale text thatspecifies either a degree of intensity or a degree of likelihoodassociated with an option of the multiple options, cause a presentation,on the display, of at least one of the first option descriptive text,the longer term goal descriptive text, and the shorter term goaldescriptive text.
 4. The decision making augmentation system of claim 1,wherein: receiving the decision descriptive text comprises receiving anindication of selection of a decision template by the operator; theselected decision template comprises a default description of thecurrent decision; and the processor is further configured to: accept thedefault description of the current decision from the selected decisiontemplate as the decision descriptive text; and cause a presentation, onthe display, of a prompt for the operator to edit the decisiondescriptive text.
 5. The decision making augmentation system of claim 4,wherein: the selected decision template specifies a quantity of themultiple options; the selected decision template, for each option of themultiple options, comprises at least one of: a first option descriptivetext of the multiple descriptive texts that describes the option; asecond option descriptive text of the multiple descriptive texts thatdescribes a possible successful outcome of the option; and a thirdoption descriptive text of the multiple descriptive texts that describesa possible unsuccessful outcome of the option; at least one of thedecision descriptive text, and the first option descriptive textassociated with at least one option of the multiple options, comprises areference to a decision aid to provide the operator within informationconcerning a subject associated with the current decision; and theprocessor is further configured to cause a presentation, on the displayand for each option, a prompt for the operator to edit at least one ofthe first option descriptive text, the second option descriptive textand the third option descriptive text.
 6. The decision makingaugmentation system of claim 1, wherein: for each option, receiving anindication of at least one selection of scale text comprises: receivingan indication of a selection of scale text specifying a degree ofpositive intensity of seeking to achieve a possible positive outcome ofthe option; receiving an indication of a selection of scale textspecifying a degree of positive likelihood of achieving the possiblepositive outcome; receiving an indication of a selection of scale textspecifying a degree of negative intensity of seeking to avoid a possiblenegative outcome of the option; and receiving an indication of aselection of scale text specifying a degree of negative likelihood ofavoiding the possible negative outcome; and for each option, deriving adegree of overall motivation associated with the option comprises:deriving a degree of positive motivation associated with the optionbased on the degree of positive intensity and the degree of positivelikelihood; deriving a degree of negative motivation associated with theoption based on the degree of negative intensity and the degree ofnegative likelihood; and deriving the degree of overall motivationassociated with the option based on the degree of positive motivationassociated with the option and the degree of negative motivationassociated with the option.
 7. The decision making augmentation systemof claim 1, wherein: for each option, receiving an indication of atleast one selection of scale text comprises: receiving an indication ofa selection of scale text specifying a first degree of positiveintensity of seeking to achieve a possible successful outcome of theoption; receiving an indication of a selection of scale text specifyinga first degree of positive likelihood of achieving the possiblesuccessful outcome; receiving an indication of a selection of scale textspecifying a first degree of negative intensity of seeking to avoid thepossible successful outcome of the option; receiving an indication of aselection of scale text specifying a first degree of negative likelihoodof avoiding the possible successful outcome; receiving an indication ofa selection of scale text specifying a second degree of positiveintensity of seeking to achieve a possible unsuccessful outcome of theoption; receiving an indication of a selection of scale text specifyinga second degree of positive likelihood of achieving the possibleunsuccessful outcome; receiving an indication of a selection of scaletext specifying a second degree of negative intensity of seeking toavoid the possible unsuccessful outcome of the option; and receiving anindication of a selection of scale text specifying a second degree ofnegative likelihood of avoiding the possible unsuccessful outcome; andfor each option, deriving a degree of overall motivation associated withthe option comprises: deriving a first degree of positive motivationassociated with the possible successful outcome based on the firstdegree of positive intensity and the first degree of positivelikelihood; deriving a second degree of positive motivation associatedwith the possible unsuccessful outcome based on the second degree ofpositive intensity and the second degree of positive likelihood;deriving a first degree of negative motivation associated with thepossible successful outcome based on the first degree of negativeintensity and the first degree of negative likelihood; deriving a seconddegree of negative motivation associated with the possible unsuccessfuloutcome based on the second degree of negative intensity and the seconddegree of negative likelihood; and deriving the degree of overallmotivation associated with the option based on an average of the firstdegree of positive motivation and the second degree of positivemotivation, and an average of the first degree of negative motivationand the second degree of negative motivation.
 8. The decision makingaugmentation system of claim 1, wherein identifying the best option fromamong the multiple options comprises selecting the option associatedwith highest degree of overall motivation among the multiple options. 9.The decision making augmentation system of claim 1, wherein: thedecision making augmentation system is operable in either a compact modeor an expanded mode; the decision making augmentation system isinitially operated in the compact mode, and the prompt for the operatorto further consider the current decision comprises a prompt for theoperator to switch to operating the decision making augmentation systemin the expanded mode; and the processor is configured to performoperations comprising: in the compact mode, for each option, cause apresentation, on the display, of at least one prompt for the operator toselect scale texts specifying: a degree of positive intensity of seekingto achieve a possible positive outcome of the option; a degree ofpositive likelihood of achieving the possible positive outcome; a degreeof negative intensity of seeking to avoid a possible negative outcome ofthe option; and a degree of negative likelihood of avoiding the possiblenegative outcome; in the expanded mode, for each option, cause apresentation, on the display, of at least one prompt for the operator toselect scale texts specifying: a first degree of positive intensity ofseeking to achieve a possible successful outcome of the option; a firstdegree of positive likelihood of achieving the possible successfuloutcome; a first degree of negative intensity of seeking to avoid thepossible successful outcome of the option; a first degree of negativelikelihood of avoiding the possible successful outcome; a second degreeof positive intensity of seeking to achieve a possible unsuccessfuloutcome of the option; a second degree of positive likelihood ofachieving the possible unsuccessful outcome; a second degree of negativeintensity of seeking to avoid the possible unsuccessful outcome of theoption; and a second degree of negative likelihood of avoiding thepossible unsuccessful outcome.
 10. The decision making augmentationsystem of claim 1, wherein: at least a portion of the decision makingaugmentation system is incorporated into a control system of a vehicleconfigured to carry at least one of passengers and cargo; and theprocessor is configured to cause a presentation, on the display, of aprompt to select a decision template that specifies aspects of adecision that closely resembles the current decision.
 11. A method ofdecision making augmentation comprising: receiving, at a processor of adecision making augmentation system, and via a manual input deviceconfigured to enable entry of text input by an operator, a decisiondescriptive text of multiple descriptive texts, wherein the decisiondescriptive text describes a current decision comprising a selection ofone option from among multiple options; causing repeated presentation ofthe decision descriptive text on a display configured to visually guidethe operator through providing the text input, wherein the text inputcomprises at least one of the multiple descriptive texts and multipleselections of scale text; for each option of the multiple options,performing operations comprising: receiving, at the processor, and viathe manual input device, an indication of at least one selection ofscale text of the multiple selections of scale text, wherein the atleast one selection of scale text specifies either a degree of intensityof seeking to achieve or avoid a possible outcome of the option, or adegree of likelihood of achieving or avoiding the possible outcome ofthe option; and deriving, by the processor, a degree of overallmotivation associated with the option based on the at least oneselection of scale text; identifying, by the processor, a best optionfrom among the multiple options based on the degree of overallmotivation associated with each option; causing a presentation of anindication of the best option on the display; comparing, by theprocessor, the degree of overall motivation associated with the bestoption to a threshold degree of overall motivation; in response to thedegree of overall motivation associated with the best option being lessthan the threshold degree of overall motivation, causing a presentation,on the display, of: a warning that the degree of overall motivationassociated with the best option is low; and a prompt for the operator tofurther consider the current decision; comparing, by the processor, thedegree of overall motivation associated with the best option to thedegree of overall motivation associated with each other option of themultiple options; and in response to the degree of overall motivationassociated with the best option not exceeding, by at least a thresholddegree of difference in overall motivation, the degree of overallmotivation associated with at least one other option of the multipleoptions, causing a presentation, on the display of: a proximity warningthat the difference in degree of the overall motivation associated withthe best option from the overall motivation associated with at least oneother option is low; and the prompt for the operator to further considerthe current decision.
 12. The method of claim 11, further comprising:causing a presentation, on the display, of a prompt for the operator toenter at least one of: the decision descriptive text; a longer term goaldescriptive text of the multiple descriptive texts that describes alonger term goal associated with the current decision; and a shorterterm goal descriptive text of the multiple descriptive texts thatdescribes a shorter term goal associated with the current decision; andduring each presentation, on the display, of a prompt for the operatorto select scale text that specifies either a degree of intensity or adegree of likelihood associated with an option of the multiple options,causing a presentation, on the display, of at least one of the decisiondescriptive text, the longer term goal descriptive text, and the shorterterm goal descriptive text.
 13. The method of claim 12, furthercomprising: causing a presentation, on the display, of at least oneprompt for the operator to enter, for each option of the multipleoptions, at least one of: a first option descriptive text of themultiple descriptive texts that describes the option; a second optiondescriptive text of the multiple descriptive texts that describes apossible successful outcome of the option; and a third optiondescriptive text of the multiple descriptive texts that describes apossible unsuccessful outcome of the option; during each presentation,on the display, of a prompt for the operator to select scale text thatspecifies either a degree of intensity or a degree of likelihoodassociated with an option of the multiple options, causing apresentation, on the display, of at least one of the first optiondescriptive text, the longer term goal descriptive text, and the shorterterm goal descriptive text.
 14. The method of claim 11, wherein:receiving the decision descriptive text comprises receiving anindication of selection of a decision template by the operator; theselected decision template comprises a default description of thecurrent decision; and the method further comprises: accepting thedefault description of the current decision from the selected decisiontemplate as the decision descriptive text; and causing a presentation,on the display, of a prompt for the operator to edit the decisiondescriptive text.
 15. The method of claim 14, wherein: the selecteddecision template specifies a quantity of the multiple options; theselected decision template, for each option of the multiple options,comprises at least one of: a first option descriptive text of themultiple descriptive texts that describes the option; a second optiondescriptive text of the multiple descriptive texts that describes apossible successful outcome of the option; and a third optiondescriptive text of the multiple descriptive texts that describes apossible unsuccessful outcome of the option; at least one of thedecision descriptive text, and the first option descriptive textassociated with at least one option of the multiple options, comprises areference to a decision aid to provide the operator within informationconcerning a subject associated with the current decision; and themethod further comprises causing a presentation, on the display and foreach option, a prompt for the operator to edit at least one of the firstoption descriptive text, the second option descriptive text and thethird option descriptive text.
 16. The method of claim 11, wherein: foreach option, receiving an indication of at least one selection of scaletext comprises: receiving an indication of a selection of scale textspecifying a degree of positive intensity of seeking to achieve apossible positive outcome of the option; receiving an indication of aselection of scale text specifying a degree of positive likelihood ofachieving the possible positive outcome; receiving an indication of aselection of scale text specifying a degree of negative intensity ofseeking to avoid a possible negative outcome of the option; andreceiving an indication of a selection of scale text specifying a degreeof negative likelihood of avoiding the possible negative outcome; andfor each option, deriving a degree of overall motivation associated withthe option comprises: deriving a degree of positive motivationassociated with the option based on the degree of positive intensity andthe degree of positive likelihood; deriving a degree of negativemotivation associated with the option based on the degree of negativeintensity and the degree of negative likelihood; and deriving the degreeof overall motivation associated with the option based on the degree ofpositive motivation associated with the option and the degree ofnegative motivation associated with the option.
 17. The method of claim11, wherein: for each option, receiving an indication of at least oneselection of scale text comprises: receiving an indication of aselection of scale text specifying a first degree of positive intensityof seeking to achieve a possible successful outcome of the option;receiving an indication of a selection of scale text specifying a firstdegree of positive likelihood of achieving the possible successfuloutcome; receiving an indication of a selection of scale text specifyinga first degree of negative intensity of seeking to avoid the possiblesuccessful outcome of the option; receiving an indication of a selectionof scale text specifying a first degree of negative likelihood ofavoiding the possible successful outcome; receiving an indication of aselection of scale text specifying a second degree of positive intensityof seeking to achieve a possible unsuccessful outcome of the option;receiving an indication of a selection of scale text specifying a seconddegree of positive likelihood of achieving the possible unsuccessfuloutcome; receiving an indication of a selection of scale text specifyinga second degree of negative intensity of seeking to avoid the possibleunsuccessful outcome of the option; and receiving an indication of aselection of scale text specifying a second degree of negativelikelihood of avoiding the possible unsuccessful outcome; and for eachoption, deriving a degree of overall motivation associated with theoption comprises: deriving a first degree of positive motivationassociated with the possible successful outcome based on the firstdegree of positive intensity and the first degree of positivelikelihood; deriving a second degree of positive motivation associatedwith the possible unsuccessful outcome based on the second degree ofpositive intensity and the second degree of positive likelihood;deriving a first degree of negative motivation associated with thepossible successful outcome based on the first degree of negativeintensity and the first degree of negative likelihood; deriving a seconddegree of negative motivation associated with the possible unsuccessfuloutcome based on the second degree of negative intensity and the seconddegree of negative likelihood; and deriving the degree of overallmotivation associated with the option based on an average of the firstdegree of positive motivation and the second degree of positivemotivation, and an average of the first degree of negative motivationand the second degree of negative motivation.
 18. The method of claim11, wherein identifying the best option from among the multiple optionscomprises selecting the option associated with highest degree of overallmotivation among the multiple options.
 19. The method of claim 11,wherein: the decision making augmentation system is operable in either acompact mode or an expanded mode; the decision making augmentationsystem is initially operated in the compact mode, and the prompt for theoperator to further consider the current decision comprises a prompt forthe operator to switch to operating the decision making augmentationsystem in the expanded mode; and the method comprises: in the compactmode, for each option, causing a presentation, on the display, of atleast one prompt for the operator to select scale texts specifying: adegree of positive intensity of seeking to achieve a possible positiveoutcome of the option; a degree of positive likelihood of achieving thepossible positive outcome; a degree of negative intensity of seeking toavoid a possible negative outcome of the option; and a degree ofnegative likelihood of avoiding the possible negative outcome; in theexpanded mode, for each option, causing a presentation, on the display,of at least one prompt for the operator to select scale textsspecifying: a first degree of positive intensity of seeking to achieve apossible successful outcome of the option; a first degree of positivelikelihood of achieving the possible successful outcome; a first degreeof negative intensity of seeking to avoid the possible successfuloutcome of the option; a first degree of negative likelihood of avoidingthe possible successful outcome; a second degree of positive intensityof seeking to achieve a possible unsuccessful outcome of the option; asecond degree of positive likelihood of achieving the possibleunsuccessful outcome; a second degree of negative intensity of seekingto avoid the possible unsuccessful outcome of the option; and a seconddegree of negative likelihood of avoiding the possible unsuccessfuloutcome.
 20. The method of claim 11, wherein: at least a portion of thedecision making augmentation system is incorporated into a controlsystem of a vehicle configured to carry at least one of passengers andcargo; and the method comprises causing a presentation, on the display,of a prompt to select a decision template that specifies aspects of adecision that closely resembles the current decision.